{ "cells": [ { "cell_type": "markdown", "id": "382549ad-6c8f-473f-9668-2f96899aa6b5", "metadata": {}, "source": [ "### String Methods" ] }, { "cell_type": "code", "execution_count": 1, "id": "c1ba25bf-f773-46fe-b75e-27afb904ce09", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "shape: (344, 17)
studyNameSample NumberSpeciesRegionIslandStageIndividual IDClutch CompletionDate EggCulmen Length (mm)Culmen Depth (mm)Flipper Length (mm)Body Mass (g)SexDelta 15 N (o/oo)Delta 13 C (o/oo)Comments
stri64strstrstrstrstrstrstrf64f64i64i64strf64f64str
"PAL0708"1"Adelie Penguin (Pygoscelis ade…"Anvers""Torgersen""Adult, 1 Egg Stage""N1A1""Yes""11/11/07"39.118.71813750"MALE"nullnull"Not enough blood for isotopes."
"PAL0708"2"Adelie Penguin (Pygoscelis ade…"Anvers""Torgersen""Adult, 1 Egg Stage""N1A2""Yes""11/11/07"39.517.41863800"FEMALE"8.94956-24.69454null
"PAL0708"3"Adelie Penguin (Pygoscelis ade…"Anvers""Torgersen""Adult, 1 Egg Stage""N2A1""Yes""11/16/07"40.318.01953250"FEMALE"8.36821-25.33302null
"PAL0708"4"Adelie Penguin (Pygoscelis ade…"Anvers""Torgersen""Adult, 1 Egg Stage""N2A2""Yes""11/16/07"nullnullnullnullnullnullnull"Adult not sampled."
"PAL0708"5"Adelie Penguin (Pygoscelis ade…"Anvers""Torgersen""Adult, 1 Egg Stage""N3A1""Yes""11/16/07"36.719.31933450"FEMALE"8.76651-25.32426null
……………………………………………
"PAL0910"120"Gentoo penguin (Pygoscelis pap…"Anvers""Biscoe""Adult, 1 Egg Stage""N38A2""No""12/1/09"nullnullnullnullnullnullnullnull
"PAL0910"121"Gentoo penguin (Pygoscelis pap…"Anvers""Biscoe""Adult, 1 Egg Stage""N39A1""Yes""11/22/09"46.814.32154850"FEMALE"8.41151-26.13832null
"PAL0910"122"Gentoo penguin (Pygoscelis pap…"Anvers""Biscoe""Adult, 1 Egg Stage""N39A2""Yes""11/22/09"50.415.72225750"MALE"8.30166-26.04117null
"PAL0910"123"Gentoo penguin (Pygoscelis pap…"Anvers""Biscoe""Adult, 1 Egg Stage""N43A1""Yes""11/22/09"45.214.82125200"FEMALE"8.24246-26.11969null
"PAL0910"124"Gentoo penguin (Pygoscelis pap…"Anvers""Biscoe""Adult, 1 Egg Stage""N43A2""Yes""11/22/09"49.916.12135400"MALE"8.3639-26.15531null
" ], "text/plain": [ "shape: (344, 17)\n", "┌───────────┬────────┬─────────────┬────────┬───┬────────┬─────────────┬─────────────┬─────────────┐\n", "│ studyName ┆ Sample ┆ Species ┆ Region ┆ … ┆ Sex ┆ Delta 15 N ┆ Delta 13 C ┆ Comments │\n", "│ --- ┆ Number ┆ --- ┆ --- ┆ ┆ --- ┆ (o/oo) ┆ (o/oo) ┆ --- │\n", "│ str ┆ --- ┆ str ┆ str ┆ ┆ str ┆ --- ┆ --- ┆ str │\n", "│ ┆ i64 ┆ ┆ ┆ ┆ ┆ f64 ┆ f64 ┆ │\n", "╞═══════════╪════════╪═════════════╪════════╪═══╪════════╪═════════════╪═════════════╪═════════════╡\n", "│ PAL0708 ┆ 1 ┆ Adelie ┆ Anvers ┆ … ┆ MALE ┆ null ┆ null ┆ Not enough │\n", "│ ┆ ┆ Penguin ┆ ┆ ┆ ┆ ┆ ┆ blood for │\n", "│ ┆ ┆ (Pygoscelis ┆ ┆ ┆ ┆ ┆ ┆ isotopes. │\n", "│ ┆ ┆ ade… ┆ ┆ ┆ ┆ ┆ ┆ │\n", "│ PAL0708 ┆ 2 ┆ Adelie ┆ Anvers ┆ … ┆ FEMALE ┆ 8.94956 ┆ -24.69454 ┆ null │\n", "│ ┆ ┆ Penguin ┆ ┆ ┆ ┆ ┆ ┆ │\n", "│ ┆ ┆ (Pygoscelis ┆ ┆ ┆ ┆ ┆ ┆ │\n", "│ ┆ ┆ ade… ┆ ┆ ┆ ┆ ┆ ┆ │\n", "│ PAL0708 ┆ 3 ┆ Adelie ┆ Anvers ┆ … ┆ FEMALE ┆ 8.36821 ┆ -25.33302 ┆ null │\n", "│ ┆ ┆ Penguin ┆ ┆ ┆ ┆ ┆ ┆ │\n", "│ ┆ ┆ (Pygoscelis ┆ ┆ ┆ ┆ ┆ ┆ │\n", "│ ┆ ┆ ade… ┆ ┆ ┆ ┆ ┆ ┆ │\n", "│ PAL0708 ┆ 4 ┆ Adelie ┆ Anvers ┆ … ┆ null ┆ null ┆ null ┆ Adult not │\n", "│ ┆ ┆ Penguin ┆ ┆ ┆ ┆ ┆ ┆ sampled. │\n", "│ ┆ ┆ (Pygoscelis ┆ ┆ ┆ ┆ ┆ ┆ │\n", "│ ┆ ┆ ade… ┆ ┆ ┆ ┆ ┆ ┆ │\n", "│ PAL0708 ┆ 5 ┆ Adelie ┆ Anvers ┆ … ┆ FEMALE ┆ 8.76651 ┆ -25.32426 ┆ null │\n", "│ ┆ ┆ Penguin ┆ ┆ ┆ ┆ ┆ ┆ │\n", "│ ┆ ┆ (Pygoscelis ┆ ┆ ┆ ┆ ┆ ┆ │\n", "│ ┆ ┆ ade… ┆ ┆ ┆ ┆ ┆ ┆ │\n", "│ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … │\n", "│ PAL0910 ┆ 120 ┆ Gentoo ┆ Anvers ┆ … ┆ null ┆ null ┆ null ┆ null │\n", "│ ┆ ┆ penguin ┆ ┆ ┆ ┆ ┆ ┆ │\n", "│ ┆ ┆ (Pygoscelis ┆ ┆ ┆ ┆ ┆ ┆ │\n", "│ ┆ ┆ pap… ┆ ┆ ┆ ┆ ┆ ┆ │\n", "│ PAL0910 ┆ 121 ┆ Gentoo ┆ Anvers ┆ … ┆ FEMALE ┆ 8.41151 ┆ -26.13832 ┆ null │\n", "│ ┆ ┆ penguin ┆ ┆ ┆ ┆ ┆ ┆ │\n", "│ ┆ ┆ (Pygoscelis ┆ ┆ ┆ ┆ ┆ ┆ │\n", "│ ┆ ┆ pap… ┆ ┆ ┆ ┆ ┆ ┆ │\n", "│ PAL0910 ┆ 122 ┆ Gentoo ┆ Anvers ┆ … ┆ MALE ┆ 8.30166 ┆ -26.04117 ┆ null │\n", "│ ┆ ┆ penguin ┆ ┆ ┆ ┆ ┆ ┆ │\n", "│ ┆ ┆ (Pygoscelis ┆ ┆ ┆ ┆ ┆ ┆ │\n", "│ ┆ ┆ pap… ┆ ┆ ┆ ┆ ┆ ┆ │\n", "│ PAL0910 ┆ 123 ┆ Gentoo ┆ Anvers ┆ … ┆ FEMALE ┆ 8.24246 ┆ -26.11969 ┆ null │\n", "│ ┆ ┆ penguin ┆ ┆ ┆ ┆ ┆ ┆ │\n", "│ ┆ ┆ (Pygoscelis ┆ ┆ ┆ ┆ ┆ ┆ │\n", "│ ┆ ┆ pap… ┆ ┆ ┆ ┆ ┆ ┆ │\n", "│ PAL0910 ┆ 124 ┆ Gentoo ┆ Anvers ┆ … ┆ MALE ┆ 8.3639 ┆ -26.15531 ┆ null │\n", "│ ┆ ┆ penguin ┆ ┆ ┆ ┆ ┆ ┆ │\n", "│ ┆ ┆ (Pygoscelis ┆ ┆ ┆ ┆ ┆ ┆ │\n", "│ ┆ ┆ pap… ┆ ┆ ┆ ┆ ┆ ┆ │\n", "└───────────┴────────┴─────────────┴────────┴───┴────────┴─────────────┴─────────────┴─────────────┘" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import polars as pl\n", "df = pl.read_csv('penguins_lter.csv')" ] }, { "cell_type": "code", "execution_count": 2, "id": "3983b4c1-b617-4bd6-8b35-a7ddf5146410", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "shape: (344,)
Species
list[str]
["Adelie Penguin ", "Pygoscelis adeliae)"]
["Adelie Penguin ", "Pygoscelis adeliae)"]
["Adelie Penguin ", "Pygoscelis adeliae)"]
["Adelie Penguin ", "Pygoscelis adeliae)"]
["Adelie Penguin ", "Pygoscelis adeliae)"]
…
["Gentoo penguin ", "Pygoscelis papua)"]
["Gentoo penguin ", "Pygoscelis papua)"]
["Gentoo penguin ", "Pygoscelis papua)"]
["Gentoo penguin ", "Pygoscelis papua)"]
["Gentoo penguin ", "Pygoscelis papua)"]
" ], "text/plain": [ "shape: (344,)\n", "Series: 'Species' [list[str]]\n", "[\n", "\t[\"Adelie Penguin \", \"Pygoscelis adeliae)\"]\n", "\t[\"Adelie Penguin \", \"Pygoscelis adeliae)\"]\n", "\t[\"Adelie Penguin \", \"Pygoscelis adeliae)\"]\n", "\t[\"Adelie Penguin \", \"Pygoscelis adeliae)\"]\n", "\t[\"Adelie Penguin \", \"Pygoscelis adeliae)\"]\n", "\t…\n", "\t[\"Gentoo penguin \", \"Pygoscelis papua)\"]\n", "\t[\"Gentoo penguin \", \"Pygoscelis papua)\"]\n", "\t[\"Gentoo penguin \", \"Pygoscelis papua)\"]\n", "\t[\"Gentoo penguin \", \"Pygoscelis papua)\"]\n", "\t[\"Gentoo penguin \", \"Pygoscelis papua)\"]\n", "]" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['Species'].str.split('(')" ] }, { "cell_type": "code", "execution_count": 3, "id": "e1c56bd3-252c-4e53-bbd7-bbddfd223358", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "shape: (344,)
Species
str
"Adelie Penguin "
"Adelie Penguin "
"Adelie Penguin "
"Adelie Penguin "
"Adelie Penguin "
…
"Gentoo penguin "
"Gentoo penguin "
"Gentoo penguin "
"Gentoo penguin "
"Gentoo penguin "
" ], "text/plain": [ "shape: (344,)\n", "Series: 'Species' [str]\n", "[\n", "\t\"Adelie Penguin \"\n", "\t\"Adelie Penguin \"\n", "\t\"Adelie Penguin \"\n", "\t\"Adelie Penguin \"\n", "\t\"Adelie Penguin \"\n", "\t…\n", "\t\"Gentoo penguin \"\n", "\t\"Gentoo penguin \"\n", "\t\"Gentoo penguin \"\n", "\t\"Gentoo penguin \"\n", "\t\"Gentoo penguin \"\n", "]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['Species'].str.split('(').list[0]" ] }, { "cell_type": "code", "execution_count": 4, "id": "08bb01be-3f2e-4877-a69f-279a80e0921b", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "shape: (344,)
Species
str
"Adelie Penguin"
"Adelie Penguin"
"Adelie Penguin"
"Adelie Penguin"
"Adelie Penguin"
…
"Gentoo penguin"
"Gentoo penguin"
"Gentoo penguin"
"Gentoo penguin"
"Gentoo penguin"
" ], "text/plain": [ "shape: (344,)\n", "Series: 'Species' [str]\n", "[\n", "\t\"Adelie Penguin\"\n", "\t\"Adelie Penguin\"\n", "\t\"Adelie Penguin\"\n", "\t\"Adelie Penguin\"\n", "\t\"Adelie Penguin\"\n", "\t…\n", "\t\"Gentoo penguin\"\n", "\t\"Gentoo penguin\"\n", "\t\"Gentoo penguin\"\n", "\t\"Gentoo penguin\"\n", "\t\"Gentoo penguin\"\n", "]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['Species'].str.split('(').list[0].str.strip_chars()" ] }, { "cell_type": "code", "execution_count": 5, "id": "bcccb296-1cd5-4330-a2a4-30cd18450184", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "shape: (344,)
Species
str
"Pygoscelis adeliae)"
"Pygoscelis adeliae)"
"Pygoscelis adeliae)"
"Pygoscelis adeliae)"
"Pygoscelis adeliae)"
…
"Pygoscelis papua)"
"Pygoscelis papua)"
"Pygoscelis papua)"
"Pygoscelis papua)"
"Pygoscelis papua)"
" ], "text/plain": [ "shape: (344,)\n", "Series: 'Species' [str]\n", "[\n", "\t\"Pygoscelis adeliae)\"\n", "\t\"Pygoscelis adeliae)\"\n", "\t\"Pygoscelis adeliae)\"\n", "\t\"Pygoscelis adeliae)\"\n", "\t\"Pygoscelis adeliae)\"\n", "\t…\n", "\t\"Pygoscelis papua)\"\n", "\t\"Pygoscelis papua)\"\n", "\t\"Pygoscelis papua)\"\n", "\t\"Pygoscelis papua)\"\n", "\t\"Pygoscelis papua)\"\n", "]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['Species'].str.split('(').list[1]" ] }, { "cell_type": "code", "execution_count": 6, "id": "f8840e78-8eda-417f-a46c-febcb7277f3d", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "shape: (344,)
Species
str
"Pygoscelis adeliae"
"Pygoscelis adeliae"
"Pygoscelis adeliae"
"Pygoscelis adeliae"
"Pygoscelis adeliae"
…
"Pygoscelis papua"
"Pygoscelis papua"
"Pygoscelis papua"
"Pygoscelis papua"
"Pygoscelis papua"
" ], "text/plain": [ "shape: (344,)\n", "Series: 'Species' [str]\n", "[\n", "\t\"Pygoscelis adeliae\"\n", "\t\"Pygoscelis adeliae\"\n", "\t\"Pygoscelis adeliae\"\n", "\t\"Pygoscelis adeliae\"\n", "\t\"Pygoscelis adeliae\"\n", "\t…\n", "\t\"Pygoscelis papua\"\n", "\t\"Pygoscelis papua\"\n", "\t\"Pygoscelis papua\"\n", "\t\"Pygoscelis papua\"\n", "\t\"Pygoscelis papua\"\n", "]" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['Species'].str.split('(').list[1].str.head(-1)" ] }, { "cell_type": "code", "execution_count": 7, "id": "ec456168-129c-433b-b325-daf1b2b6c937", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "shape: (344, 18)
studyNameSample NumberSpeciesRegionIslandStageIndividual IDClutch CompletionDate EggCulmen Length (mm)Culmen Depth (mm)Flipper Length (mm)Body Mass (g)SexDelta 15 N (o/oo)Delta 13 C (o/oo)CommentsScientificSpecies
stri64strstrstrstrstrstrstrf64f64i64i64strf64f64strstr
"PAL0708"1"Adelie Penguin""Anvers""Torgersen""Adult, 1 Egg Stage""N1A1""Yes""11/11/07"39.118.71813750"MALE"nullnull"Not enough blood for isotopes.""Pygoscelis adeliae"
"PAL0708"2"Adelie Penguin""Anvers""Torgersen""Adult, 1 Egg Stage""N1A2""Yes""11/11/07"39.517.41863800"FEMALE"8.94956-24.69454null"Pygoscelis adeliae"
"PAL0708"3"Adelie Penguin""Anvers""Torgersen""Adult, 1 Egg Stage""N2A1""Yes""11/16/07"40.318.01953250"FEMALE"8.36821-25.33302null"Pygoscelis adeliae"
"PAL0708"4"Adelie Penguin""Anvers""Torgersen""Adult, 1 Egg Stage""N2A2""Yes""11/16/07"nullnullnullnullnullnullnull"Adult not sampled.""Pygoscelis adeliae"
"PAL0708"5"Adelie Penguin""Anvers""Torgersen""Adult, 1 Egg Stage""N3A1""Yes""11/16/07"36.719.31933450"FEMALE"8.76651-25.32426null"Pygoscelis adeliae"
………………………………………………
"PAL0910"120"Gentoo penguin""Anvers""Biscoe""Adult, 1 Egg Stage""N38A2""No""12/1/09"nullnullnullnullnullnullnullnull"Pygoscelis papua"
"PAL0910"121"Gentoo penguin""Anvers""Biscoe""Adult, 1 Egg Stage""N39A1""Yes""11/22/09"46.814.32154850"FEMALE"8.41151-26.13832null"Pygoscelis papua"
"PAL0910"122"Gentoo penguin""Anvers""Biscoe""Adult, 1 Egg Stage""N39A2""Yes""11/22/09"50.415.72225750"MALE"8.30166-26.04117null"Pygoscelis papua"
"PAL0910"123"Gentoo penguin""Anvers""Biscoe""Adult, 1 Egg Stage""N43A1""Yes""11/22/09"45.214.82125200"FEMALE"8.24246-26.11969null"Pygoscelis papua"
"PAL0910"124"Gentoo penguin""Anvers""Biscoe""Adult, 1 Egg Stage""N43A2""Yes""11/22/09"49.916.12135400"MALE"8.3639-26.15531null"Pygoscelis papua"
" ], "text/plain": [ "shape: (344, 18)\n", "┌───────────┬────────┬─────────┬────────┬───┬─────────────┬─────────────┬─────────────┬────────────┐\n", "│ studyName ┆ Sample ┆ Species ┆ Region ┆ … ┆ Delta 15 N ┆ Delta 13 C ┆ Comments ┆ Scientific │\n", "│ --- ┆ Number ┆ --- ┆ --- ┆ ┆ (o/oo) ┆ (o/oo) ┆ --- ┆ Species │\n", "│ str ┆ --- ┆ str ┆ str ┆ ┆ --- ┆ --- ┆ str ┆ --- │\n", "│ ┆ i64 ┆ ┆ ┆ ┆ f64 ┆ f64 ┆ ┆ str │\n", "╞═══════════╪════════╪═════════╪════════╪═══╪═════════════╪═════════════╪═════════════╪════════════╡\n", "│ PAL0708 ┆ 1 ┆ Adelie ┆ Anvers ┆ … ┆ null ┆ null ┆ Not enough ┆ Pygoscelis │\n", "│ ┆ ┆ Penguin ┆ ┆ ┆ ┆ ┆ blood for ┆ adeliae │\n", "│ ┆ ┆ ┆ ┆ ┆ ┆ ┆ isotopes. ┆ │\n", "│ PAL0708 ┆ 2 ┆ Adelie ┆ Anvers ┆ … ┆ 8.94956 ┆ -24.69454 ┆ null ┆ Pygoscelis │\n", "│ ┆ ┆ Penguin ┆ ┆ ┆ ┆ ┆ ┆ adeliae │\n", "│ PAL0708 ┆ 3 ┆ Adelie ┆ Anvers ┆ … ┆ 8.36821 ┆ -25.33302 ┆ null ┆ Pygoscelis │\n", "│ ┆ ┆ Penguin ┆ ┆ ┆ ┆ ┆ ┆ adeliae │\n", "│ PAL0708 ┆ 4 ┆ Adelie ┆ Anvers ┆ … ┆ null ┆ null ┆ Adult not ┆ Pygoscelis │\n", "│ ┆ ┆ Penguin ┆ ┆ ┆ ┆ ┆ sampled. ┆ adeliae │\n", "│ PAL0708 ┆ 5 ┆ Adelie ┆ Anvers ┆ … ┆ 8.76651 ┆ -25.32426 ┆ null ┆ Pygoscelis │\n", "│ ┆ ┆ Penguin ┆ ┆ ┆ ┆ ┆ ┆ adeliae │\n", "│ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … │\n", "│ PAL0910 ┆ 120 ┆ Gentoo ┆ Anvers ┆ … ┆ null ┆ null ┆ null ┆ Pygoscelis │\n", "│ ┆ ┆ penguin ┆ ┆ ┆ ┆ ┆ ┆ papua │\n", "│ PAL0910 ┆ 121 ┆ Gentoo ┆ Anvers ┆ … ┆ 8.41151 ┆ -26.13832 ┆ null ┆ Pygoscelis │\n", "│ ┆ ┆ penguin ┆ ┆ ┆ ┆ ┆ ┆ papua │\n", "│ PAL0910 ┆ 122 ┆ Gentoo ┆ Anvers ┆ … ┆ 8.30166 ┆ -26.04117 ┆ null ┆ Pygoscelis │\n", "│ ┆ ┆ penguin ┆ ┆ ┆ ┆ ┆ ┆ papua │\n", "│ PAL0910 ┆ 123 ┆ Gentoo ┆ Anvers ┆ … ┆ 8.24246 ┆ -26.11969 ┆ null ┆ Pygoscelis │\n", "│ ┆ ┆ penguin ┆ ┆ ┆ ┆ ┆ ┆ papua │\n", "│ PAL0910 ┆ 124 ┆ Gentoo ┆ Anvers ┆ … ┆ 8.3639 ┆ -26.15531 ┆ null ┆ Pygoscelis │\n", "│ ┆ ┆ penguin ┆ ┆ ┆ ┆ ┆ ┆ papua │\n", "└───────────┴────────┴─────────┴────────┴───┴─────────────┴─────────────┴─────────────┴────────────┘" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.with_columns(pl.col('Species').str.split('(').list[0].str.strip_chars(),\n", " pl.col('Species').str.split('(').list[1].str.head(-1).alias(\"ScientificSpecies\"))" ] }, { "cell_type": "code", "execution_count": 21, "id": "c1b00523-8a5d-4db6-bdfc-65555deded95", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "shape: (688, 17)
studyNameSample NumberSpeciesRegionIslandStageIndividual IDClutch CompletionDate EggCulmen Length (mm)Culmen Depth (mm)Flipper Length (mm)Body Mass (g)SexDelta 15 N (o/oo)Delta 13 C (o/oo)Comments
stri64strstrstrstrstrstrstrf64f64i64i64strf64f64str
"PAL0708"1"Adelie Penguin ""Anvers""Torgersen""Adult, 1 Egg Stage""N1A1""Yes""11/11/07"39.118.71813750"MALE"nullnull"Not enough blood for isotopes."
"PAL0708"1"Pygoscelis adeliae)""Anvers""Torgersen""Adult, 1 Egg Stage""N1A1""Yes""11/11/07"39.118.71813750"MALE"nullnull"Not enough blood for isotopes."
"PAL0708"2"Adelie Penguin ""Anvers""Torgersen""Adult, 1 Egg Stage""N1A2""Yes""11/11/07"39.517.41863800"FEMALE"8.94956-24.69454null
"PAL0708"2"Pygoscelis adeliae)""Anvers""Torgersen""Adult, 1 Egg Stage""N1A2""Yes""11/11/07"39.517.41863800"FEMALE"8.94956-24.69454null
"PAL0708"3"Adelie Penguin ""Anvers""Torgersen""Adult, 1 Egg Stage""N2A1""Yes""11/16/07"40.318.01953250"FEMALE"8.36821-25.33302null
……………………………………………
"PAL0910"122"Pygoscelis papua)""Anvers""Biscoe""Adult, 1 Egg Stage""N39A2""Yes""11/22/09"50.415.72225750"MALE"8.30166-26.04117null
"PAL0910"123"Gentoo penguin ""Anvers""Biscoe""Adult, 1 Egg Stage""N43A1""Yes""11/22/09"45.214.82125200"FEMALE"8.24246-26.11969null
"PAL0910"123"Pygoscelis papua)""Anvers""Biscoe""Adult, 1 Egg Stage""N43A1""Yes""11/22/09"45.214.82125200"FEMALE"8.24246-26.11969null
"PAL0910"124"Gentoo penguin ""Anvers""Biscoe""Adult, 1 Egg Stage""N43A2""Yes""11/22/09"49.916.12135400"MALE"8.3639-26.15531null
"PAL0910"124"Pygoscelis papua)""Anvers""Biscoe""Adult, 1 Egg Stage""N43A2""Yes""11/22/09"49.916.12135400"MALE"8.3639-26.15531null
" ], "text/plain": [ "shape: (688, 17)\n", "┌───────────┬────────┬─────────────┬────────┬───┬────────┬─────────────┬─────────────┬─────────────┐\n", "│ studyName ┆ Sample ┆ Species ┆ Region ┆ … ┆ Sex ┆ Delta 15 N ┆ Delta 13 C ┆ Comments │\n", "│ --- ┆ Number ┆ --- ┆ --- ┆ ┆ --- ┆ (o/oo) ┆ (o/oo) ┆ --- │\n", "│ str ┆ --- ┆ str ┆ str ┆ ┆ str ┆ --- ┆ --- ┆ str │\n", "│ ┆ i64 ┆ ┆ ┆ ┆ ┆ f64 ┆ f64 ┆ │\n", "╞═══════════╪════════╪═════════════╪════════╪═══╪════════╪═════════════╪═════════════╪═════════════╡\n", "│ PAL0708 ┆ 1 ┆ Adelie ┆ Anvers ┆ … ┆ MALE ┆ null ┆ null ┆ Not enough │\n", "│ ┆ ┆ Penguin ┆ ┆ ┆ ┆ ┆ ┆ blood for │\n", "│ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ isotopes. │\n", "│ PAL0708 ┆ 1 ┆ Pygoscelis ┆ Anvers ┆ … ┆ MALE ┆ null ┆ null ┆ Not enough │\n", "│ ┆ ┆ adeliae) ┆ ┆ ┆ ┆ ┆ ┆ blood for │\n", "│ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ isotopes. │\n", "│ PAL0708 ┆ 2 ┆ Adelie ┆ Anvers ┆ … ┆ FEMALE ┆ 8.94956 ┆ -24.69454 ┆ null │\n", "│ ┆ ┆ Penguin ┆ ┆ ┆ ┆ ┆ ┆ │\n", "│ PAL0708 ┆ 2 ┆ Pygoscelis ┆ Anvers ┆ … ┆ FEMALE ┆ 8.94956 ┆ -24.69454 ┆ null │\n", "│ ┆ ┆ adeliae) ┆ ┆ ┆ ┆ ┆ ┆ │\n", "│ PAL0708 ┆ 3 ┆ Adelie ┆ Anvers ┆ … ┆ FEMALE ┆ 8.36821 ┆ -25.33302 ┆ null │\n", "│ ┆ ┆ Penguin ┆ ┆ ┆ ┆ ┆ ┆ │\n", "│ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … │\n", "│ PAL0910 ┆ 122 ┆ Pygoscelis ┆ Anvers ┆ … ┆ MALE ┆ 8.30166 ┆ -26.04117 ┆ null │\n", "│ ┆ ┆ papua) ┆ ┆ ┆ ┆ ┆ ┆ │\n", "│ PAL0910 ┆ 123 ┆ Gentoo ┆ Anvers ┆ … ┆ FEMALE ┆ 8.24246 ┆ -26.11969 ┆ null │\n", "│ ┆ ┆ penguin ┆ ┆ ┆ ┆ ┆ ┆ │\n", "│ PAL0910 ┆ 123 ┆ Pygoscelis ┆ Anvers ┆ … ┆ FEMALE ┆ 8.24246 ┆ -26.11969 ┆ null │\n", "│ ┆ ┆ papua) ┆ ┆ ┆ ┆ ┆ ┆ │\n", "│ PAL0910 ┆ 124 ┆ Gentoo ┆ Anvers ┆ … ┆ MALE ┆ 8.3639 ┆ -26.15531 ┆ null │\n", "│ ┆ ┆ penguin ┆ ┆ ┆ ┆ ┆ ┆ │\n", "│ PAL0910 ┆ 124 ┆ Pygoscelis ┆ Anvers ┆ … ┆ MALE ┆ 8.3639 ┆ -26.15531 ┆ null │\n", "│ ┆ ┆ papua) ┆ ┆ ┆ ┆ ┆ ┆ │\n", "└───────────┴────────┴─────────────┴────────┴───┴────────┴─────────────┴─────────────┴─────────────┘" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.with_columns(pl.col('Species').str.split('(')).explode('Species')" ] }, { "cell_type": "code", "execution_count": 8, "id": "f7bd01a4-fa14-4e6b-bcbd-343688dcf086", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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studyNameSample NumberSpeciesRegionIslandStageIndividual IDClutch CompletionDate EggCulmen Length (mm)Culmen Depth (mm)Flipper Length (mm)Body Mass (g)SexDelta 15 N (o/oo)Delta 13 C (o/oo)Comments
0PAL07081Adelie Penguin (Pygoscelis adeliae)AnversTorgersenAdult, 1 Egg StageN1A1Yes11/11/0739.118.7181.03750.0MALENaNNaNNot enough blood for isotopes.
1PAL07082Adelie Penguin (Pygoscelis adeliae)AnversTorgersenAdult, 1 Egg StageN1A2Yes11/11/0739.517.4186.03800.0FEMALE8.94956-24.69454NaN
2PAL07083Adelie Penguin (Pygoscelis adeliae)AnversTorgersenAdult, 1 Egg StageN2A1Yes11/16/0740.318.0195.03250.0FEMALE8.36821-25.33302NaN
3PAL07084Adelie Penguin (Pygoscelis adeliae)AnversTorgersenAdult, 1 Egg StageN2A2Yes11/16/07NaNNaNNaNNaNNaNNaNNaNAdult not sampled.
4PAL07085Adelie Penguin (Pygoscelis adeliae)AnversTorgersenAdult, 1 Egg StageN3A1Yes11/16/0736.719.3193.03450.0FEMALE8.76651-25.32426NaN
......................................................
339PAL0910120Gentoo penguin (Pygoscelis papua)AnversBiscoeAdult, 1 Egg StageN38A2No12/1/09NaNNaNNaNNaNNaNNaNNaNNaN
340PAL0910121Gentoo penguin (Pygoscelis papua)AnversBiscoeAdult, 1 Egg StageN39A1Yes11/22/0946.814.3215.04850.0FEMALE8.41151-26.13832NaN
341PAL0910122Gentoo penguin (Pygoscelis papua)AnversBiscoeAdult, 1 Egg StageN39A2Yes11/22/0950.415.7222.05750.0MALE8.30166-26.04117NaN
342PAL0910123Gentoo penguin (Pygoscelis papua)AnversBiscoeAdult, 1 Egg StageN43A1Yes11/22/0945.214.8212.05200.0FEMALE8.24246-26.11969NaN
343PAL0910124Gentoo penguin (Pygoscelis papua)AnversBiscoeAdult, 1 Egg StageN43A2Yes11/22/0949.916.1213.05400.0MALE8.36390-26.15531NaN
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344 rows × 17 columns

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" ], "text/plain": [ " studyName Sample Number Species Region \\\n", "0 PAL0708 1 Adelie Penguin (Pygoscelis adeliae) Anvers \n", "1 PAL0708 2 Adelie Penguin (Pygoscelis adeliae) Anvers \n", "2 PAL0708 3 Adelie Penguin (Pygoscelis adeliae) Anvers \n", "3 PAL0708 4 Adelie Penguin (Pygoscelis adeliae) Anvers \n", "4 PAL0708 5 Adelie Penguin (Pygoscelis adeliae) Anvers \n", ".. ... ... ... ... \n", "339 PAL0910 120 Gentoo penguin (Pygoscelis papua) Anvers \n", "340 PAL0910 121 Gentoo penguin (Pygoscelis papua) Anvers \n", "341 PAL0910 122 Gentoo penguin (Pygoscelis papua) Anvers \n", "342 PAL0910 123 Gentoo penguin (Pygoscelis papua) Anvers \n", "343 PAL0910 124 Gentoo penguin (Pygoscelis papua) Anvers \n", "\n", " Island Stage Individual ID Clutch Completion Date Egg \\\n", "0 Torgersen Adult, 1 Egg Stage N1A1 Yes 11/11/07 \n", "1 Torgersen Adult, 1 Egg Stage N1A2 Yes 11/11/07 \n", "2 Torgersen Adult, 1 Egg Stage N2A1 Yes 11/16/07 \n", "3 Torgersen Adult, 1 Egg Stage N2A2 Yes 11/16/07 \n", "4 Torgersen Adult, 1 Egg Stage N3A1 Yes 11/16/07 \n", ".. ... ... ... ... ... \n", "339 Biscoe Adult, 1 Egg Stage N38A2 No 12/1/09 \n", "340 Biscoe Adult, 1 Egg Stage N39A1 Yes 11/22/09 \n", "341 Biscoe Adult, 1 Egg Stage N39A2 Yes 11/22/09 \n", "342 Biscoe Adult, 1 Egg Stage N43A1 Yes 11/22/09 \n", "343 Biscoe Adult, 1 Egg Stage N43A2 Yes 11/22/09 \n", "\n", " Culmen Length (mm) Culmen Depth (mm) Flipper Length (mm) \\\n", "0 39.1 18.7 181.0 \n", "1 39.5 17.4 186.0 \n", "2 40.3 18.0 195.0 \n", "3 NaN NaN NaN \n", "4 36.7 19.3 193.0 \n", ".. ... ... ... \n", "339 NaN NaN NaN \n", "340 46.8 14.3 215.0 \n", "341 50.4 15.7 222.0 \n", "342 45.2 14.8 212.0 \n", "343 49.9 16.1 213.0 \n", "\n", " Body Mass (g) Sex Delta 15 N (o/oo) Delta 13 C (o/oo) \\\n", "0 3750.0 MALE NaN NaN \n", "1 3800.0 FEMALE 8.94956 -24.69454 \n", "2 3250.0 FEMALE 8.36821 -25.33302 \n", "3 NaN NaN NaN NaN \n", "4 3450.0 FEMALE 8.76651 -25.32426 \n", ".. ... ... ... ... \n", "339 NaN NaN NaN NaN \n", "340 4850.0 FEMALE 8.41151 -26.13832 \n", "341 5750.0 MALE 8.30166 -26.04117 \n", "342 5200.0 FEMALE 8.24246 -26.11969 \n", "343 5400.0 MALE 8.36390 -26.15531 \n", "\n", " Comments \n", "0 Not enough blood for isotopes. \n", "1 NaN \n", "2 NaN \n", "3 Adult not sampled. \n", "4 NaN \n", ".. ... \n", "339 NaN \n", "340 NaN \n", "341 NaN \n", "342 NaN \n", "343 NaN \n", "\n", "[344 rows x 17 columns]" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "dfa = pd.read_csv('penguins_lter.csv')" ] }, { "cell_type": "code", "execution_count": 9, "id": "cde951e9-b6b8-4686-a55e-207e2d04a899", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 [Adelie Penguin , Pygoscelis adeliae)]\n", "1 [Adelie Penguin , Pygoscelis adeliae)]\n", "2 [Adelie Penguin , Pygoscelis adeliae)]\n", "3 [Adelie Penguin , Pygoscelis adeliae)]\n", "4 [Adelie Penguin , Pygoscelis adeliae)]\n", " ... \n", "339 [Gentoo penguin , Pygoscelis papua)]\n", "340 [Gentoo penguin , Pygoscelis papua)]\n", "341 [Gentoo penguin , Pygoscelis papua)]\n", "342 [Gentoo penguin , Pygoscelis papua)]\n", "343 [Gentoo penguin , Pygoscelis papua)]\n", "Name: Species, Length: 344, dtype: object" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dfa.Species.str.split('(')" ] }, { "cell_type": "code", "execution_count": 10, "id": "7ca75384-a94f-45ac-a524-a19a7cf6e313", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 Adelie Penguin \n", "1 Adelie Penguin \n", "2 Adelie Penguin \n", "3 Adelie Penguin \n", "4 Adelie Penguin \n", " ... \n", "339 Gentoo penguin \n", "340 Gentoo penguin \n", "341 Gentoo penguin \n", "342 Gentoo penguin \n", "343 Gentoo penguin \n", "Name: Species, Length: 344, dtype: object" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dfa.Species.str.split('(').str[0]" ] }, { "cell_type": "code", "execution_count": 11, "id": "479c7698-b6d3-4a0d-a859-971b5f6a0f62", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'Adelie Penguin '" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dfa.Species.str.split('(').str[0].loc[0]" ] }, { "cell_type": "code", "execution_count": 12, "id": "9882662c-a4c8-47d9-8d70-3e70a659f0d2", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 Adelie Penguin\n", "1 Adelie Penguin\n", "2 Adelie Penguin\n", "3 Adelie Penguin\n", "4 Adelie Penguin\n", " ... \n", "339 Gentoo penguin\n", "340 Gentoo penguin\n", "341 Gentoo penguin\n", "342 Gentoo penguin\n", "343 Gentoo penguin\n", "Name: Species, Length: 344, dtype: object" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dfa.Species.str.split('(').str[0].str.strip()" ] }, { "cell_type": "code", "execution_count": 13, "id": "c7fca70b-7d6e-4d4f-b890-09f13ca4a999", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 Pygoscelis adeliae)\n", "1 Pygoscelis adeliae)\n", "2 Pygoscelis adeliae)\n", "3 Pygoscelis adeliae)\n", "4 Pygoscelis adeliae)\n", " ... \n", "339 Pygoscelis papua)\n", "340 Pygoscelis papua)\n", "341 Pygoscelis papua)\n", "342 Pygoscelis papua)\n", "343 Pygoscelis papua)\n", "Name: Species, Length: 344, dtype: object" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dfa.Species.str.split('(').str[1]" ] }, { "cell_type": "code", "execution_count": 14, "id": "6c90e3be-c772-49aa-922f-73dad5c5c005", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 Pygoscelis adeliae\n", "1 Pygoscelis adeliae\n", "2 Pygoscelis adeliae\n", "3 Pygoscelis adeliae\n", "4 Pygoscelis adeliae\n", " ... \n", "339 Pygoscelis papua\n", "340 Pygoscelis papua\n", "341 Pygoscelis papua\n", "342 Pygoscelis papua\n", "343 Pygoscelis papua\n", "Name: Species, Length: 344, dtype: object" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dfa.Species.str.split('(').str[1].str[:-1]" ] }, { "cell_type": "code", "execution_count": 15, "id": "b690ec9d-80e5-4933-9a4e-82d454c71ff9", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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studyNameSample NumberSpeciesRegionIslandStageIndividual IDClutch CompletionDate EggCulmen Length (mm)Culmen Depth (mm)Flipper Length (mm)Body Mass (g)SexDelta 15 N (o/oo)Delta 13 C (o/oo)CommentsScientificSpecies
0PAL07081Adelie PenguinAnversTorgersenAdult, 1 Egg StageN1A1Yes11/11/0739.118.7181.03750.0MALENaNNaNNot enough blood for isotopes.Pygoscelis adeliae
1PAL07082Adelie PenguinAnversTorgersenAdult, 1 Egg StageN1A2Yes11/11/0739.517.4186.03800.0FEMALE8.94956-24.69454NaNPygoscelis adeliae
2PAL07083Adelie PenguinAnversTorgersenAdult, 1 Egg StageN2A1Yes11/16/0740.318.0195.03250.0FEMALE8.36821-25.33302NaNPygoscelis adeliae
3PAL07084Adelie PenguinAnversTorgersenAdult, 1 Egg StageN2A2Yes11/16/07NaNNaNNaNNaNNaNNaNNaNAdult not sampled.Pygoscelis adeliae
4PAL07085Adelie PenguinAnversTorgersenAdult, 1 Egg StageN3A1Yes11/16/0736.719.3193.03450.0FEMALE8.76651-25.32426NaNPygoscelis adeliae
.........................................................
339PAL0910120Gentoo penguinAnversBiscoeAdult, 1 Egg StageN38A2No12/1/09NaNNaNNaNNaNNaNNaNNaNNaNPygoscelis papua
340PAL0910121Gentoo penguinAnversBiscoeAdult, 1 Egg StageN39A1Yes11/22/0946.814.3215.04850.0FEMALE8.41151-26.13832NaNPygoscelis papua
341PAL0910122Gentoo penguinAnversBiscoeAdult, 1 Egg StageN39A2Yes11/22/0950.415.7222.05750.0MALE8.30166-26.04117NaNPygoscelis papua
342PAL0910123Gentoo penguinAnversBiscoeAdult, 1 Egg StageN43A1Yes11/22/0945.214.8212.05200.0FEMALE8.24246-26.11969NaNPygoscelis papua
343PAL0910124Gentoo penguinAnversBiscoeAdult, 1 Egg StageN43A2Yes11/22/0949.916.1213.05400.0MALE8.36390-26.15531NaNPygoscelis papua
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344 rows × 18 columns

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" ], "text/plain": [ " studyName Sample Number Species Region Island \\\n", "0 PAL0708 1 Adelie Penguin Anvers Torgersen \n", "1 PAL0708 2 Adelie Penguin Anvers Torgersen \n", "2 PAL0708 3 Adelie Penguin Anvers Torgersen \n", "3 PAL0708 4 Adelie Penguin Anvers Torgersen \n", "4 PAL0708 5 Adelie Penguin Anvers Torgersen \n", ".. ... ... ... ... ... \n", "339 PAL0910 120 Gentoo penguin Anvers Biscoe \n", "340 PAL0910 121 Gentoo penguin Anvers Biscoe \n", "341 PAL0910 122 Gentoo penguin Anvers Biscoe \n", "342 PAL0910 123 Gentoo penguin Anvers Biscoe \n", "343 PAL0910 124 Gentoo penguin Anvers Biscoe \n", "\n", " Stage Individual ID Clutch Completion Date Egg \\\n", "0 Adult, 1 Egg Stage N1A1 Yes 11/11/07 \n", "1 Adult, 1 Egg Stage N1A2 Yes 11/11/07 \n", "2 Adult, 1 Egg Stage N2A1 Yes 11/16/07 \n", "3 Adult, 1 Egg Stage N2A2 Yes 11/16/07 \n", "4 Adult, 1 Egg Stage N3A1 Yes 11/16/07 \n", ".. ... ... ... ... \n", "339 Adult, 1 Egg Stage N38A2 No 12/1/09 \n", "340 Adult, 1 Egg Stage N39A1 Yes 11/22/09 \n", "341 Adult, 1 Egg Stage N39A2 Yes 11/22/09 \n", "342 Adult, 1 Egg Stage N43A1 Yes 11/22/09 \n", "343 Adult, 1 Egg Stage N43A2 Yes 11/22/09 \n", "\n", " Culmen Length (mm) Culmen Depth (mm) Flipper Length (mm) \\\n", "0 39.1 18.7 181.0 \n", "1 39.5 17.4 186.0 \n", "2 40.3 18.0 195.0 \n", "3 NaN NaN NaN \n", "4 36.7 19.3 193.0 \n", ".. ... ... ... \n", "339 NaN NaN NaN \n", "340 46.8 14.3 215.0 \n", "341 50.4 15.7 222.0 \n", "342 45.2 14.8 212.0 \n", "343 49.9 16.1 213.0 \n", "\n", " Body Mass (g) Sex Delta 15 N (o/oo) Delta 13 C (o/oo) \\\n", "0 3750.0 MALE NaN NaN \n", "1 3800.0 FEMALE 8.94956 -24.69454 \n", "2 3250.0 FEMALE 8.36821 -25.33302 \n", "3 NaN NaN NaN NaN \n", "4 3450.0 FEMALE 8.76651 -25.32426 \n", ".. ... ... ... ... \n", "339 NaN NaN NaN NaN \n", "340 4850.0 FEMALE 8.41151 -26.13832 \n", "341 5750.0 MALE 8.30166 -26.04117 \n", "342 5200.0 FEMALE 8.24246 -26.11969 \n", "343 5400.0 MALE 8.36390 -26.15531 \n", "\n", " Comments ScientificSpecies \n", "0 Not enough blood for isotopes. Pygoscelis adeliae \n", "1 NaN Pygoscelis adeliae \n", "2 NaN Pygoscelis adeliae \n", "3 Adult not sampled. Pygoscelis adeliae \n", "4 NaN Pygoscelis adeliae \n", ".. ... ... \n", "339 NaN Pygoscelis papua \n", "340 NaN Pygoscelis papua \n", "341 NaN Pygoscelis papua \n", "342 NaN Pygoscelis papua \n", "343 NaN Pygoscelis papua \n", "\n", "[344 rows x 18 columns]" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dfa.assign(Species=dfa.Species.str.split('(').str[0].str.strip(),\n", " ScientificSpecies=dfa.Species.str.split('(').str[1].str[:-1])" ] }, { "cell_type": "code", "execution_count": 22, "id": "868e3754-c875-4845-ad1b-115973b28ec1", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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studyNameSample NumberSpeciesRegionIslandStageIndividual IDClutch CompletionDate EggCulmen Length (mm)Culmen Depth (mm)Flipper Length (mm)Body Mass (g)SexDelta 15 N (o/oo)Delta 13 C (o/oo)Comments
0PAL07081Adelie PenguinAnversTorgersenAdult, 1 Egg StageN1A1Yes11/11/0739.118.7181.03750.0MALENaNNaNNot enough blood for isotopes.
0PAL07081Pygoscelis adeliae)AnversTorgersenAdult, 1 Egg StageN1A1Yes11/11/0739.118.7181.03750.0MALENaNNaNNot enough blood for isotopes.
1PAL07082Adelie PenguinAnversTorgersenAdult, 1 Egg StageN1A2Yes11/11/0739.517.4186.03800.0FEMALE8.94956-24.69454NaN
1PAL07082Pygoscelis adeliae)AnversTorgersenAdult, 1 Egg StageN1A2Yes11/11/0739.517.4186.03800.0FEMALE8.94956-24.69454NaN
2PAL07083Adelie PenguinAnversTorgersenAdult, 1 Egg StageN2A1Yes11/16/0740.318.0195.03250.0FEMALE8.36821-25.33302NaN
......................................................
341PAL0910122Pygoscelis papua)AnversBiscoeAdult, 1 Egg StageN39A2Yes11/22/0950.415.7222.05750.0MALE8.30166-26.04117NaN
342PAL0910123Gentoo penguinAnversBiscoeAdult, 1 Egg StageN43A1Yes11/22/0945.214.8212.05200.0FEMALE8.24246-26.11969NaN
342PAL0910123Pygoscelis papua)AnversBiscoeAdult, 1 Egg StageN43A1Yes11/22/0945.214.8212.05200.0FEMALE8.24246-26.11969NaN
343PAL0910124Gentoo penguinAnversBiscoeAdult, 1 Egg StageN43A2Yes11/22/0949.916.1213.05400.0MALE8.36390-26.15531NaN
343PAL0910124Pygoscelis papua)AnversBiscoeAdult, 1 Egg StageN43A2Yes11/22/0949.916.1213.05400.0MALE8.36390-26.15531NaN
\n", "

688 rows × 17 columns

\n", "
" ], "text/plain": [ " studyName Sample Number Species Region Island \\\n", "0 PAL0708 1 Adelie Penguin Anvers Torgersen \n", "0 PAL0708 1 Pygoscelis adeliae) Anvers Torgersen \n", "1 PAL0708 2 Adelie Penguin Anvers Torgersen \n", "1 PAL0708 2 Pygoscelis adeliae) Anvers Torgersen \n", "2 PAL0708 3 Adelie Penguin Anvers Torgersen \n", ".. ... ... ... ... ... \n", "341 PAL0910 122 Pygoscelis papua) Anvers Biscoe \n", "342 PAL0910 123 Gentoo penguin Anvers Biscoe \n", "342 PAL0910 123 Pygoscelis papua) Anvers Biscoe \n", "343 PAL0910 124 Gentoo penguin Anvers Biscoe \n", "343 PAL0910 124 Pygoscelis papua) Anvers Biscoe \n", "\n", " Stage Individual ID Clutch Completion Date Egg \\\n", "0 Adult, 1 Egg Stage N1A1 Yes 11/11/07 \n", "0 Adult, 1 Egg Stage N1A1 Yes 11/11/07 \n", "1 Adult, 1 Egg Stage N1A2 Yes 11/11/07 \n", "1 Adult, 1 Egg Stage N1A2 Yes 11/11/07 \n", "2 Adult, 1 Egg Stage N2A1 Yes 11/16/07 \n", ".. ... ... ... ... \n", "341 Adult, 1 Egg Stage N39A2 Yes 11/22/09 \n", "342 Adult, 1 Egg Stage N43A1 Yes 11/22/09 \n", "342 Adult, 1 Egg Stage N43A1 Yes 11/22/09 \n", "343 Adult, 1 Egg Stage N43A2 Yes 11/22/09 \n", "343 Adult, 1 Egg Stage N43A2 Yes 11/22/09 \n", "\n", " Culmen Length (mm) Culmen Depth (mm) Flipper Length (mm) \\\n", "0 39.1 18.7 181.0 \n", "0 39.1 18.7 181.0 \n", "1 39.5 17.4 186.0 \n", "1 39.5 17.4 186.0 \n", "2 40.3 18.0 195.0 \n", ".. ... ... ... \n", "341 50.4 15.7 222.0 \n", "342 45.2 14.8 212.0 \n", "342 45.2 14.8 212.0 \n", "343 49.9 16.1 213.0 \n", "343 49.9 16.1 213.0 \n", "\n", " Body Mass (g) Sex Delta 15 N (o/oo) Delta 13 C (o/oo) \\\n", "0 3750.0 MALE NaN NaN \n", "0 3750.0 MALE NaN NaN \n", "1 3800.0 FEMALE 8.94956 -24.69454 \n", "1 3800.0 FEMALE 8.94956 -24.69454 \n", "2 3250.0 FEMALE 8.36821 -25.33302 \n", ".. ... ... ... ... \n", "341 5750.0 MALE 8.30166 -26.04117 \n", "342 5200.0 FEMALE 8.24246 -26.11969 \n", "342 5200.0 FEMALE 8.24246 -26.11969 \n", "343 5400.0 MALE 8.36390 -26.15531 \n", "343 5400.0 MALE 8.36390 -26.15531 \n", "\n", " Comments \n", "0 Not enough blood for isotopes. \n", "0 Not enough blood for isotopes. \n", "1 NaN \n", "1 NaN \n", "2 NaN \n", ".. ... \n", "341 NaN \n", "342 NaN \n", "342 NaN \n", "343 NaN \n", "343 NaN \n", "\n", "[688 rows x 17 columns]" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dfa.assign(Species=lambda dfa: dfa['Species'].str.split('(')).explode('Species')" ] }, { "cell_type": "markdown", "id": "c00382d8-5daf-4923-b883-c61e15960b7b", "metadata": {}, "source": [ "### DateTime Methods" ] }, { "cell_type": "code", "execution_count": 16, "id": "d3051e9e-d3f5-45fb-b13f-38e0936dbdc9", "metadata": {}, "outputs": [ { "ename": "InvalidOperationError", "evalue": "conversion from `str` to `date` failed in column 'Date Egg' for 226 out of 344 values: [\"11/16/07\", \"11/16/07\", … \"11/16/07\"]\n\nYou might want to try:\n- setting `strict=False` to set values that cannot be converted to `null`\n- using `str.strptime`, `str.to_date`, or `str.to_datetime` and providing a format string", "output_type": "error", "traceback": [ "\u001b[31m---------------------------------------------------------------------------\u001b[39m", "\u001b[31mInvalidOperationError\u001b[39m Traceback (most recent call last)", "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[16]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[43mdf\u001b[49m\u001b[43m[\u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mDate Egg\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m.\u001b[49m\u001b[43mstr\u001b[49m\u001b[43m.\u001b[49m\u001b[43mto_date\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", "\u001b[36mFile \u001b[39m\u001b[32m~/Dropbox/Documents/Teaching/cs503-2026sp/.pixi/envs/default/lib/python3.14/site-packages/polars/series/utils.py:106\u001b[39m, in \u001b[36mcall_expr..wrapper\u001b[39m\u001b[34m(self, *args, **kwargs)\u001b[39m\n\u001b[32m 104\u001b[39m expr = \u001b[38;5;28mgetattr\u001b[39m(expr, namespace)\n\u001b[32m 105\u001b[39m f = \u001b[38;5;28mgetattr\u001b[39m(expr, func.\u001b[34m__name__\u001b[39m)\n\u001b[32m--> \u001b[39m\u001b[32m106\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43ms\u001b[49m\u001b[43m.\u001b[49m\u001b[43mto_frame\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m.\u001b[49m\u001b[43mselect_seq\u001b[49m\u001b[43m(\u001b[49m\u001b[43mf\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m.to_series()\n", "\u001b[36mFile \u001b[39m\u001b[32m~/Dropbox/Documents/Teaching/cs503-2026sp/.pixi/envs/default/lib/python3.14/site-packages/polars/dataframe/frame.py:10181\u001b[39m, in \u001b[36mDataFrame.select_seq\u001b[39m\u001b[34m(self, *exprs, **named_exprs)\u001b[39m\n\u001b[32m 10156\u001b[39m \u001b[38;5;250m\u001b[39m\u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m 10157\u001b[39m \u001b[33;03mSelect columns from this DataFrame.\u001b[39;00m\n\u001b[32m 10158\u001b[39m \n\u001b[32m (...)\u001b[39m\u001b[32m 10174\u001b[39m \u001b[33;03mselect\u001b[39;00m\n\u001b[32m 10175\u001b[39m \u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m 10176\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mpolars\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mlazyframe\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mopt_flags\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m QueryOptFlags\n\u001b[32m 10178\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m (\n\u001b[32m 10179\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mlazy\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 10180\u001b[39m \u001b[43m \u001b[49m\u001b[43m.\u001b[49m\u001b[43mselect_seq\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43mexprs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mnamed_exprs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m> \u001b[39m\u001b[32m10181\u001b[39m \u001b[43m \u001b[49m\u001b[43m.\u001b[49m\u001b[43mcollect\u001b[49m\u001b[43m(\u001b[49m\u001b[43moptimizations\u001b[49m\u001b[43m=\u001b[49m\u001b[43mQueryOptFlags\u001b[49m\u001b[43m.\u001b[49m\u001b[43m_eager\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 10182\u001b[39m )\n", "\u001b[36mFile \u001b[39m\u001b[32m~/Dropbox/Documents/Teaching/cs503-2026sp/.pixi/envs/default/lib/python3.14/site-packages/polars/_utils/deprecation.py:97\u001b[39m, in \u001b[36mdeprecate_streaming_parameter..decorate..wrapper\u001b[39m\u001b[34m(*args, **kwargs)\u001b[39m\n\u001b[32m 93\u001b[39m kwargs[\u001b[33m\"\u001b[39m\u001b[33mengine\u001b[39m\u001b[33m\"\u001b[39m] = \u001b[33m\"\u001b[39m\u001b[33min-memory\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 95\u001b[39m \u001b[38;5;28;01mdel\u001b[39;00m kwargs[\u001b[33m\"\u001b[39m\u001b[33mstreaming\u001b[39m\u001b[33m\"\u001b[39m]\n\u001b[32m---> \u001b[39m\u001b[32m97\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunction\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", "\u001b[36mFile \u001b[39m\u001b[32m~/Dropbox/Documents/Teaching/cs503-2026sp/.pixi/envs/default/lib/python3.14/site-packages/polars/lazyframe/opt_flags.py:328\u001b[39m, in \u001b[36mforward_old_opt_flags..decorate..wrapper\u001b[39m\u001b[34m(*args, **kwargs)\u001b[39m\n\u001b[32m 325\u001b[39m optflags = cb(optflags, kwargs.pop(key)) \u001b[38;5;66;03m# type: ignore[no-untyped-call,unused-ignore]\u001b[39;00m\n\u001b[32m 327\u001b[39m kwargs[\u001b[33m\"\u001b[39m\u001b[33moptimizations\u001b[39m\u001b[33m\"\u001b[39m] = optflags\n\u001b[32m--> \u001b[39m\u001b[32m328\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunction\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", "\u001b[36mFile \u001b[39m\u001b[32m~/Dropbox/Documents/Teaching/cs503-2026sp/.pixi/envs/default/lib/python3.14/site-packages/polars/lazyframe/frame.py:2429\u001b[39m, in \u001b[36mLazyFrame.collect\u001b[39m\u001b[34m(self, type_coercion, predicate_pushdown, projection_pushdown, simplify_expression, slice_pushdown, comm_subplan_elim, comm_subexpr_elim, cluster_with_columns, collapse_joins, no_optimization, engine, background, optimizations, **_kwargs)\u001b[39m\n\u001b[32m 2427\u001b[39m \u001b[38;5;66;03m# Only for testing purposes\u001b[39;00m\n\u001b[32m 2428\u001b[39m callback = _kwargs.get(\u001b[33m\"\u001b[39m\u001b[33mpost_opt_callback\u001b[39m\u001b[33m\"\u001b[39m, callback)\n\u001b[32m-> \u001b[39m\u001b[32m2429\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m wrap_df(\u001b[43mldf\u001b[49m\u001b[43m.\u001b[49m\u001b[43mcollect\u001b[49m\u001b[43m(\u001b[49m\u001b[43mengine\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcallback\u001b[49m\u001b[43m)\u001b[49m)\n", "\u001b[31mInvalidOperationError\u001b[39m: conversion from `str` to `date` failed in column 'Date Egg' for 226 out of 344 values: [\"11/16/07\", \"11/16/07\", … \"11/16/07\"]\n\nYou might want to try:\n- setting `strict=False` to set values that cannot be converted to `null`\n- using `str.strptime`, `str.to_date`, or `str.to_datetime` and providing a format string" ] } ], "source": [ "df['Date Egg'].str.to_date()" ] }, { "cell_type": "code", "execution_count": 17, "id": "e009da2c-3242-49c0-9fef-7f8c60cc7108", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "shape: (344,)
Date Egg
date
2007-11-11
2007-11-11
2007-11-16
2007-11-16
2007-11-16
…
2009-12-01
2009-11-22
2009-11-22
2009-11-22
2009-11-22
" ], "text/plain": [ "shape: (344,)\n", "Series: 'Date Egg' [date]\n", "[\n", "\t2007-11-11\n", "\t2007-11-11\n", "\t2007-11-16\n", "\t2007-11-16\n", "\t2007-11-16\n", "\t…\n", "\t2009-12-01\n", "\t2009-11-22\n", "\t2009-11-22\n", "\t2009-11-22\n", "\t2009-11-22\n", "]" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['Date Egg'].str.to_date('%m/%d/%y')" ] }, { "cell_type": "code", "execution_count": 18, "id": "2505a0f8-5ec9-4cdf-ac88-aa2edb162f35", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "shape: (344,)
Date Egg
i32
2007
2007
2007
2007
2007
…
2009
2009
2009
2009
2009
" ], "text/plain": [ "shape: (344,)\n", "Series: 'Date Egg' [i32]\n", "[\n", "\t2007\n", "\t2007\n", "\t2007\n", "\t2007\n", "\t2007\n", "\t…\n", "\t2009\n", "\t2009\n", "\t2009\n", "\t2009\n", "\t2009\n", "]" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['Date Egg'].str.to_date('%m/%d/%y').dt.year()" ] }, { "cell_type": "code", "execution_count": 19, "id": "a99c99ef-ad5e-49bd-ae0a-fc9108fcef37", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/var/folders/wh/31nq3r6s42jgshq2j9skw_vw0000gn/T/ipykernel_5355/3421390971.py:1: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " pd.to_datetime(dfa['Date Egg'])\n" ] }, { "data": { "text/plain": [ "0 2007-11-11\n", "1 2007-11-11\n", "2 2007-11-16\n", "3 2007-11-16\n", "4 2007-11-16\n", " ... \n", "339 2009-12-01\n", "340 2009-11-22\n", "341 2009-11-22\n", "342 2009-11-22\n", "343 2009-11-22\n", "Name: Date Egg, Length: 344, dtype: datetime64[ns]" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.to_datetime(dfa['Date Egg'])" ] }, { "cell_type": "code", "execution_count": 20, "id": "fe71310e-74bf-49ae-b8a9-3edc29391e8b", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/var/folders/wh/31nq3r6s42jgshq2j9skw_vw0000gn/T/ipykernel_5355/3709367808.py:1: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " pd.to_datetime(dfa['Date Egg']).dt.year\n" ] }, { "data": { "text/plain": [ "0 2007\n", "1 2007\n", "2 2007\n", "3 2007\n", "4 2007\n", " ... \n", "339 2009\n", "340 2009\n", "341 2009\n", "342 2009\n", "343 2009\n", "Name: Date Egg, Length: 344, dtype: int32" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.to_datetime(dfa['Date Egg']).dt.year" ] }, { "cell_type": "markdown", "id": "00f174fd-a47d-4b73-8b1b-567183361278", "metadata": { "tags": [] }, "source": [ "## matplotlib Visualization" ] }, { "cell_type": "code", "execution_count": 23, "id": "c3a4dd63-9d40-4c2b-a5e5-438fb05a32ed", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "plt.plot([1,5,2,7,3]) # just y values" ] }, { "cell_type": "code", "execution_count": 24, "id": "ed973457-15a7-4073-af29-0a2b36f1699a", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot([1,3,4,6,10],[1,5,2,7,3]) # x and y values" ] }, { "cell_type": "code", "execution_count": 25, "id": "272a5cf6-ffcb-4ce4-b495-65d74b01fbe1", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import numpy as np\n", "plt.plot(np.array([1,3,4,6,10]),np.array([1,5,2,7,3])) # x and y values" ] }, { "cell_type": "code", "execution_count": 26, "id": "56d5ea1e-fd08-4ab7-96ba-65a80b640bcc", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.scatter([1,3,4,6,10],[1,5,2,7,3]) # different version" ] }, { "cell_type": "code", "execution_count": 29, "id": "c6faf4cc-edcd-4686-bcae-100da7aa6eef", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot([1,3,4,6,10],[1,5,2,7,3], 'o--') # add format string" ] }, { "cell_type": "code", "execution_count": 30, "id": "df32800d-7ae0-4df1-8149-c4c7691aa977", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# red color\n", "# square marker\n", "# dashed line\n", "plt.plot([1,3,4,6,10],[1,5,2,7,3],'rs--')" ] }, { "cell_type": "code", "execution_count": 31, "id": "9e4e9013-8fbe-44bb-b95f-dba56c05bbf6", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot([1,3,4,6,10],[1,5,2,7,3], \n", " color='red', marker='s', linestyle='dashed',\n", " linewidth=3, markersize=12)" ] }, { "cell_type": "code", "execution_count": 33, "id": "fd155b69-971c-4b89-b247-8727901ab465", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "data = {'age': [1,3,4,6,10],\n", " 'num_jumps': [1,5,2,7,3],\n", " 'weight': [20,50,25,55,125],\n", " 'num_scoops': [3,2,4,2,3]}\n", "plt.scatter(x='age',y='num_jumps', c='num_scoops', s='weight', data=data) #,c='num_scoops',s='weight',data=data)" ] }, { "cell_type": "markdown", "id": "3543a3e7-26df-45ed-9dba-1aed7018f624", "metadata": {}, "source": [ "# Other matplotlib plots" ] }, { "cell_type": "code", "execution_count": 34, "id": "2e6b66e6-cfb2-41d9-a267-a77ca9c48199", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "plt.plot([3,5,12,13],'ro-')" ] }, { "cell_type": "code", "execution_count": 35, "id": "6fffd45a-753f-446b-acbc-048e2a066a92", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.bar(['Apple','Banana','Orange'],[0.99,0.50,1.25])" ] }, { "cell_type": "code", "execution_count": 36, "id": "85e84dc9-a960-4ce6-82d9-f4d8ff744101", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import numpy as np\n", "np.random.seed(19680801)\n", "Z = np.random.rand(6, 10)\n", "x = np.arange(-0.5, 10, 1) # len = 11\n", "y = np.arange(4.5, 11, 1) # len = 7\n", "\n", "plt.pcolormesh(x, y, Z)" ] }, { "cell_type": "code", "execution_count": 37, "id": "6089e7c5-55d7-4c28-a07e-bec29c217ccf", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "([,\n", " ,\n", " ,\n", " ],\n", " [Text(0.8899186825075615, 0.6465637930815537, 'Apple'),\n", " Text(-0.8899187390319623, 0.6465637152823859, 'Banana'),\n", " Text(1.311736850028664e-08, -1.0999999999999999, 'Orange'),\n", " Text(1.046162345950534, -0.33991814590467956, 'Pear')])" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.pie([20,40,30,10], labels=['Apple','Banana','Orange','Pear'])" ] }, { "cell_type": "code", "execution_count": 38, "id": "ea6469fd-157a-4176-838a-a663d2513abb", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 1.0, 'Kangaroo Jumps Today')" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot([1,3,4,6,10],[1,5,2,7,3])\n", "plt.xlabel('Age')\n", "plt.ylabel('Number of Jumps')\n", "plt.title('Kangaroo Jumps Today')" ] }, { "cell_type": "markdown", "id": "4088ad9c-24e7-47ac-b50e-3523e7840fe3", "metadata": {}, "source": [ "# Altair" ] }, { "cell_type": "code", "execution_count": 39, "id": "07403262-7c2e-4bd8-8b74-c06485fd72a9", "metadata": {}, "outputs": [], "source": [ "# may need to install altair\n", "# %conda install -c conda-forge altair\n", "\n", "import altair as alt\n", "import pandas as pd" ] }, { "cell_type": "code", "execution_count": 40, "id": "de5d9d9e-6074-4b77-85bf-80fb2735a681", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " x y\n", "0 1 1\n", "1 3 5\n", "2 4 2\n", "3 6 7\n", "4 10 3" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data = pd.DataFrame({\n", " 'x': [1,3,4,6,10],\n", " 'y': [1,5,2,7,3]\n", "})" ] }, { "cell_type": "code", "execution_count": 41, "id": "e0840e5b-de7f-4a25-beaa-36a2ebf5933f", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "
\n", "" ], "text/plain": [ "alt.Chart(...)" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ "alt.Chart(data).mark_line().encode(\n", " x='x',\n", " y='y'\n", ")" ] }, { "cell_type": "markdown", "id": "e6d7139d-a7f7-41ff-8918-8f75241924df", "metadata": {}, "source": [ "### Attribute Types" ] }, { "cell_type": "code", "execution_count": 42, "id": "8ce1463c-4568-4336-b51a-ad37c7a88383", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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ageweightzoo_areanum_scoops
012013
135032
242534
365512
41012523
\n", "
" ], "text/plain": [ " age weight zoo_area num_scoops\n", "0 1 20 1 3\n", "1 3 50 3 2\n", "2 4 25 3 4\n", "3 6 55 1 2\n", "4 10 125 2 3" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data = pd.DataFrame({\n", " 'age': [1,3,4,6,10],\n", " 'weight': [20,50,25,55,125],\n", " 'zoo_area': [1,3,3,1,2],\n", " 'num_scoops': [3,2,4,2,3]\n", "})" ] }, { "cell_type": "code", "execution_count": 43, "id": "ee55dcd1-560b-4501-b804-1a5bf2b851f3", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "
\n", "" ], "text/plain": [ "alt.Chart(...)" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ "alt.Chart(data).mark_point(filled=True, size=100, stroke='black',strokeWidth=1).encode(\n", " x='age',\n", " y='weight',\n", " color='zoo_area' # inferred as :Q\n", ")" ] }, { "cell_type": "code", "execution_count": 44, "id": "33508a97-301e-463a-990d-ecef760f1527", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "
\n", "" ], "text/plain": [ "alt.Chart(...)" ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" } ], "source": [ "alt.Chart(data).mark_point(filled=True, size=100, stroke='black',strokeWidth=1).encode(\n", " x='age',\n", " y='weight',\n", " color='zoo_area:O'\n", ")" ] }, { "cell_type": "code", "execution_count": 45, "id": "1da507ba-8c37-406a-8cd9-599f3946da5e", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "
\n", "" ], "text/plain": [ "alt.Chart(...)" ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], "source": [ "alt.Chart(data).mark_point(filled=True, size=100, stroke='black',strokeWidth=1).encode(\n", " x='age',\n", " y='weight',\n", " color='zoo_area:N'\n", ")" ] }, { "cell_type": "code", "execution_count": 46, "id": "81bc0a99-2a97-4402-84ca-b6a5939c1516", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "
\n", "" ], "text/plain": [ "alt.Chart(...)" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Bubble Chart\n", "\n", "data = pd.DataFrame({\n", " 'age': [1,3,4,6,10, 11,7,5,9,9],\n", " 'weight': [20,50,25,55,125, 20,50,25,55,125],\n", " 'zoo_area': [1,3,3,1,2, 1,3,3,1,2],\n", " 'num_scoops': [3,2,4,2,3,10,5,7,90,120]\n", "})\n", "\n", "alt.Chart(data).mark_point(filled=True, stroke='black',strokeWidth=1).encode(\n", " x='age',\n", " y='weight',\n", " color='zoo_area:N',\n", " size='num_scoops'\n", ")" ] }, { "cell_type": "markdown", "id": "6558b4a6-56b7-419f-aa88-9eb85969d1d3", "metadata": {}, "source": [ "### Labels" ] }, { "cell_type": "code", "execution_count": 47, "id": "31d56f3e-0c2e-4a0a-a1b0-2e86e457e599", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 1.0, 'Kangaroo Jumps Today')" ] }, "execution_count": 47, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "\n", "plt.plot([1,3,4,6,10],[1,5,2,7,3])\n", "plt.xlabel('Age')\n", "plt.ylabel('Number of Jumps')\n", "plt.title('Kangaroo Jumps Today')" ] }, { "cell_type": "code", "execution_count": 48, "id": "8012e7b6-3384-4a98-a207-e7520ef51d7c", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots()\n", "ax.scatter([1,3,4,6,10],[1,5,2,7,3])" ] }, { "cell_type": "code", "execution_count": 49, "id": "f70dd529-54fa-44b8-a4ed-6f3a4dcab302", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 1.0, 'Kangaroo Jumps Today')" ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots()\n", "ax.scatter([1,3,4,6,10],[1,5,2,7,3])\n", "ax.set_xlabel('Age')\n", "ax.set_ylabel('Number of Jumps')\n", "ax.set_title('Kangaroo Jumps Today')" ] }, { "cell_type": "code", "execution_count": 50, "id": "2c353713-95b0-47d8-bb48-07c807a66b4e", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots()\n", "ax.scatter([1,3,4,6,10],[1,5,2,7,3])\n", "ax.set_xlabel('Age')\n", "ax.set_ylabel('Number of Jumps')\n", "ax.set_title('Kangaroo Jumps Today')\n", "fig.tight_layout()" ] }, { "cell_type": "code", "execution_count": 51, "id": "5b32f9ce-b4d0-4d50-b2a6-56e8215458f4", "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import numpy as np\n", "np.random.seed(19680801)\n", "Z = np.random.rand(6, 10)\n", "x = np.arange(-0.5, 10, 1) # len = 11\n", "y = np.arange(4.5, 11, 1) # len = 7\n", "\n", "fig, ax = plt.subplots(2,2,figsize=(15,10))\n", "ax[0,0].plot([1,3,4,6,10],[1,5,2,7,3])\n", "# ax[0,0].set_xlabel('Age')\n", "ax[0,1].bar(['Apple','Banana','Orange'],[0.99,0.50,1.25])\n", "ax[1,0].pcolormesh(x, y, Z)\n", "ax[1,1].pie([20,40,30,10], labels=['Apple','Banana','Orange','Pear'])\n", "# plt.xlabel(\"Age\")\n", "fig.savefig('vis.png')" ] }, { "cell_type": "markdown", "id": "a989592f-e3ed-44ae-918d-8906b4d7672c", "metadata": {}, "source": [ "# Plotting with pandas" ] }, { "cell_type": "code", "execution_count": 52, "id": "17478cf8-320c-4026-96b5-4fae9e731320", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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namecountprice
0Apple200.99
1Banana400.50
2Orange301.25
3Pear101.25
\n", "
" ], "text/plain": [ " name count price\n", "0 Apple 20 0.99\n", "1 Banana 40 0.50\n", "2 Orange 30 1.25\n", "3 Pear 10 1.25" ] }, "execution_count": 52, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "fruit = pd.DataFrame({'name': ['Apple','Banana','Orange','Pear'],\n", " 'count': [20,40,30,10],\n", " 'price': [0.99,0.50,1.25,1.25]})" ] }, { "cell_type": "code", "execution_count": 53, "id": "19eff909-70ed-4d21-96a6-4187b19afb92", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 53, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fruit.plot(kind='bar',x='name',y='count')\n", "# plt.title(\"Fruit Counts\")" ] }, { "cell_type": "code", "execution_count": 55, "id": "11f376d8-28c6-4554-a491-61cae1952b13", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 55, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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P2332s5+Nc845J0aMGPFm9hcA6MhhZNu2bbFs2bIYPXp0s+U5v2TJkla3u/vuu+NHP/pRXHPNNfu+pwBAu9SlLStv2LAhduzYEX369Gm2POfXrVvX4jY//OEP48orr6z6lWR/kb2xdevWaqrZvHlzW3YTAGjvHVjr6uqazTc0NOy2LGVwyaaZ6667Lo4//vi9fv5Zs2ZFr169GqdsBgIA2qc2hZHevXtH586dd6sFWb9+/W61JWnLli2xdOnS+NznPlfViuQ0c+bM+Nd//dfq/9/+9rdbfJ3p06dXHV5r0+rVq9v6dwEA7bGZpmvXrtVQ3kWLFsUnP/nJxuU5f+aZZ+62fs+ePeP555/fbVhwhpAHHnigGh7ckm7dulUTAND+tSmMpGnTpsW5554bw4cPr0bG3H777dWw3smTJzfWaqxZsybuueee6NSpUwwZMqTZ9kceeWR1fZJdlwMAHVObw8j48eNj48aNVXPL2rVrq1CxcOHCGDhwYPV4LvtV1xwBANjnMJKmTJlSTS2ZN2/eHre99tprqwkAILk3DQBQlDACABQljAAARQkjAEBRwggAUJQwAgAUJYwAAEUJIwBAUcIIAFCUMAIAFCWMAABFCSMAQFHCCABQlDACABQljAAARQkjAEBRwggAUJQwAgAUJYwAAEUJIwBAUcIIAFCUMAIAFCWMAABFCSMAQFHCCABQlDACABQljAAARQkjAEBRwggAUJQwAgAUJYwAAEUJIwBAUcIIAFCUMAIAFCWMAABFCSMAQFHCCABQlDACABQljAAARQkjAEBRwggAUJQwAgAUJYwAAEUJIwBAUcIIAFCUMAIAHHxhZM6cOTFo0KDo3r17DBs2LBYvXtzqug899FCcdtpp8c53vjN69uwZI0aMiMcff/zN7DMA0JHDyPz582Pq1KkxY8aMWL58eYwaNSrGjBkTq1atanH9p556qgojCxcujGXLlsUpp5wS48aNq7YFAGhzGJk9e3ZMnDgxJk2aFIMHD476+vro379/zJ07t8X18/HPf/7z8cEPfjCOO+64uOGGG6p/H330Ue8+ANC2MLJt27aqdmP06NHNluf8kiVL9uo5du7cGVu2bInDDz+81XW2bt0amzdvbjYBAO1Tm8LIhg0bYseOHdGnT59my3N+3bp1e/UcN998c7z22mtx9tlnt7rOrFmzolevXo1T1rwAAO3TPnVgraurazbf0NCw27KW3H///XHttddW/U6OPPLIVtebPn16bNq0qXFavXr1vuwmAHAQ6NKWlXv37h2dO3ferRZk/fr1u9WW7CoDSPY1WbBgQZx66ql7XLdbt27VBAC0f22qGenatWs1lHfRokXNluf8yJEj91gjcv7558d9990XZ5xxxr7vLQDQsWtG0rRp0+Lcc8+N4cOHV9cMuf3226thvZMnT25sYlmzZk3cc889jUFkwoQJccstt8SJJ57YWKvSo0ePqj8IANCxtTmMjB8/PjZu3BgzZ86MtWvXxpAhQ6priAwcOLB6PJc1vebIbbfdFtu3b48LL7ywmmrOO++8mDdv3v76OwCAjhJG0pQpU6qpJbsGjCeeeGLf9gwA6BDcmwYAKEoYAQCKEkYAgKKEEQCgKGEEAChKGAEAihJGAICihBEAoChhBAAoShgBAIoSRgCAooQRAKAoYQQAKEoYAQCKEkYAgKKEEQCgKGEEAChKGAEAihJGAICihBEAoChhBAAoShgBAIoSRgCAooQRAKAoYQQAKEoYAQCKEkYAgKKEEQCgKGEEAChKGAEAihJGAICihBEAoChhBAAoShgBAIoSRgCAooQRAKAoYQQAKEoYAQCKEkYAgKKEEQCgKGEEAChKGAEAihJGAICihBEAoChhBAA4+MLInDlzYtCgQdG9e/cYNmxYLF68eI/rP/nkk9V6uf6xxx4bX/3qV/d1fwGAjh5G5s+fH1OnTo0ZM2bE8uXLY9SoUTFmzJhYtWpVi+uvXLkyxo4dW62X61911VVx8cUXx4MPPrg/9h8A6GhhZPbs2TFx4sSYNGlSDB48OOrr66N///4xd+7cFtfPWpABAwZU6+X6ud1nPvOZuOmmm/bH/gMAB7kubVl527ZtsWzZsrjyyiubLR89enQsWbKkxW2+853vVI83dfrpp8edd94Zb7zxRhxyyCG7bbN169Zqqtm0aVP17+bNm2N/27n19f3+nOydA1GeTSnbcpRt+3Ugy9Z3tv2Va+15Gxoa9l8Y2bBhQ+zYsSP69OnTbHnOr1u3rsVtcnlL62/fvr16vr59++62zaxZs+K6667bbXnWwNB+9KovvQccKMq2/VK27VOvA3w83rJlS/Tq1Wv/hJGaurq6ZvOZeHZd9qvWb2l5zfTp02PatGmN8zt37oyf//znccQRR+zxdTqaTJwZ0FavXh09e/YsvTvsR8q2fVKu7ZeybVn+3mcQ6devX+xJm8JI7969o3PnzrvVgqxfv3632o+ao446qsX1u3TpUoWLlnTr1q2amnrHO97Rll3tUDKICCPtk7Jtn5Rr+6Vsd7enGpF96sDatWvXaojuokWLmi3P+ZEjR7a4zYgRI3Zb/1vf+lYMHz68xf4iAEDH0ubRNNl8cscdd8Rdd90VK1asiEsvvbQa1jt58uTGJpYJEyY0rp/Lf/KTn1Tb5fq5XXZevfzyy/fvXwIAHJTa3Gdk/PjxsXHjxpg5c2asXbs2hgwZEgsXLoyBAwdWj+eyptccyYuj5eMZWr7yla9U7UZf/vKX46yzztq/f0kHlE1Z11xzzW5NWhz8lG37pFzbL2X75tQ1/KrxNgAAB5B70wAARQkjAEBRwggAUJQw0o5de+218f73v7/0bgDAHgkjBeX9fPIich//+MdL7gZtdP7551dXAq5NefG+LMN/+7d/8162E3lV47whaI7+y+sr5WjBSy65pBpJSMf6juf1sI499tjqchSvvfZa6V1rt4SRgvKaKxdddFE8/fTTzYZD879fho8cxp7TP/3TP1VXFP7t3/7t0rvFfvDyyy9XF2X8wQ9+EPfff3+89NJL1d3Hs5zzIo55a4rWbiRK+/uO5+fh+uuvjzlz5hyw62Nt89kRRkrJhP33f//38Sd/8ifVj9i8efMaH3viiSeqRP7Nb34zhg4dGt27d48PfehD8fzzzzeuk+vnJfIfeeSROP7446t1TjvttOqMbk/uvvvuGDx4cLX+e9/73uoLxr5dUyBvdZBTNoX96Z/+afXe/9d//Vf1eM5nuRx66KHVWdWf//mfV3ep3rUJ7e/+7u/imGOOqS6X/Pu///vVPRxqHnvssfjwhz9clXPWvuTn5Ec/+lHj4z/+8Y+rz8lDDz0Up5xySvVa+XnJO2XX5Jn8pz/96Tj66KOrx3/zN3+z+oGldRdeeGFVG5JXiv7IRz4SAwYMiDFjxsQ//uM/xpo1a2LGjBnVellu+SOVZ9FZfn/0R3+038o+//8Hf/AHcdhhh1U3E/3Sl74UJ598ckydOrXZD9jnP//5eNe73lWtl8eIPHawf7/jef+vc845pyqPPN7m1TBuvPHGqmx79OhRfeceeOCBxu3yZrJZq5bX2MrHf/3Xfz1uueWWZs+dn5lPfOIT1U1hs/bt+OOP7/DFpmakkPnz51cf0pz+8A//sAoJu17y5Yorroibbropvve978WRRx4Zv/M7v9PsoPb666/HX/zFX8Tf/u3fxjPPPFPdqCkPaq35m7/5m+pAmtvk1XBvuOGG6kCZ27PvXn311bj33nvjPe95T+P9lt7+9rdXgfHFF1+sDkT53ucPSlMZLPLg9o1vfKOannzyyfjiF7/YLLDmlYuz/POsvFOnTvHJT36yunFkU1mmecb23HPPVQe1DB95V+z0P//zP9UtHPL5//3f/z3++I//OM4999z47ne/q8hbkLUejz/+eEyZMqX6IWkqf5jyBym/u7Xv6l/91V9VF35ctmxZ9V3aX2Wf5Z7f6a9//evV7TQWL14c3//+95s9xwUXXFCt87Wvfa1qIvy93/u96mz+hz/8obI9APLzkMffP/uzP6uO13Pnzo0XXnihuqBnHsOzDFN+PzP858lmfgauvvrquOqqq6r5pvI7ncfhLN9vfOMbyiwvesZbb+TIkQ319fXV/994442G3r17NyxatKia/+d//uc80jV87Wtfa1x/48aNDT169GiYP39+NX/33XdX6zz77LON66xYsaJa9t3vfreav+aaaxqGDh3a+Hj//v0b7rvvvmb78YUvfKFhxIgRB/ivbV/OO++8hs6dOzccdthh1ZTved++fRuWLVvW6jY33nhjw7Bhwxrns2wOPfTQhs2bNzcuu+KKKxo+9KEPtfoc69evr17r+eefr+ZXrlxZzd9xxx2N67zwwgvVsvwstGbs2LENl112WZv+5o4iv0/5/j388MMtPj579uzq8VdeeaVh4MCBDZ/4xCd+5XO2texz+SGHHNKwYMGCxsd/8YtfVNtccskl1fxLL73UUFdX17BmzZpmr/Wxj32sYfr06fvwl7Prd/zMM89snM9j6hFHHNHwqU99qqF79+4NS5Ysabb+xIkTGz796U+3+iZOmTKl4ayzzmr2/H369GnYunWrN/7/afPl4Hnz/vM//zP+5V/+papeT9nfIC+zn31ITj311Mb1sn265vDDD69qUTJJ1+R22bZdk80uWaWf65xwwgnNXjObD2qd8mrVySnPoPfmjoo0l80ieWZUO5vO5q6sys9yzc6OWW1bX19f9TfImpN8n3e9s3JW0edZdE1Wx+cdrZuePefZ9rPPPhsbNmxorBHJ/kV5Nl7zvve9r9lzpHye/DxklXGecefZfDYxbN26tZqyWp+2q9WIZPNYavr9q3mzZZ99FPIMvOl3OL+j+f2vyVqS3Jddq/ezbFu7Gzptk7UVb3vb26ryy/I488wzqxrILN9sEm8qm8w+8IEPNM5nH6O8h1vel+2Xv/xl9fiuIxuzyTSbA/m/hJEC8kaB+QHPtt6aPLBkr+3//u//3uO2tYNga/OtLav9kGWVcbYtN5Ujemib/DHPZpmabArJH4x8f7NvRzaXXXfddXH66adXy7Mq/eabb272HLvetTrLrWkTzLhx46r26nzObFfOxzKE7NrZrenz1Mq+9jz5mtlEkD+OefDL/c5+BzrMtSzLNN/DrF7PNv1d/cd//Ef82q/9WvTu3bvxc9BUBsc3W/a7Bp6aps24uW5+b7N5aNfvb/6Asv9OOLKs8vuX/9aaN7M/X9Pjd6rdIyybY7LpJss8TygzdGZz3q5No04ImhNG3mIZQu65557qgzp69Ohmj+XNA7PvQe2sNw9s2XkuZUjJ3v15ttv0uZYuXdp4BpU1Lr/4xS+arVPTp0+f6suTZ13Z7s3+lT8c2acjz4KyHT9rR2odHVOeIbVFdjzNGq7bbrstRo0aVS3LUVdtlX0N8owu27RrP2LZpyA7MbO7rFXIs96s6coflKb9RtatW1d9P/Ou5C0F/rQ/yv7d73539cOXtWwZRlP2B8tyyw61Kc/Cs9Yra1Nqnw8O7AlH+o3f+I0qdGTtZK0sWvrOjRw5sup3VNO04zktE0YKVP1lsMjmkl2bRz71qU9VtSa1zm55Z+Q8OGaQyINbno01PVvLA1YODc67IOf/P/e5z8WJJ564WxNN0178F198cVVlnE0KWaWbYSb3JzvMsffyvcsfp5Tv36233lpVyWdtxqZNm6qDVZ4Rf/CDH6zOoh5++OE2vb159p1lf/vtt1dV+Pl8V155ZZuLKA+mDz74YHVNm3zO2bNnV/stjLQuyzJ/TLJmI0fL5KiI7KiYHcoz0GcH8D2932+27PNM+rzzzqteL5tns/N63p07w24tBGXzTJ5UZDDKE5sMJ9mU9+1vf7uqARs7dmybXpO9L5tsqsmgmsE+R7tlUMzvV9ZIZbnlZyBPOLMjdH52ctRUdkLP/9M6o2neYhk2sl9IS/00smYkR0TUes1nW39eaCmbAHK8e/asb9rGmEMHcxhhDjvL6sA8i8uDYGsmTZpUtWNmT/88YGWyz//7krRdDrvNkJBTNnvlwWbBggXV8MusiciDVYbDbCfOA1VtpMXeyh+eLMushs+asny+rOptq3zd3/qt36p+WHPfckRIS80P/H/HHXdcFdKzhiL7cuW/OQopq+1z2HQGhNbsj7JPGRrzO51Nfnm8OOmkkxqH5NfkiI4MI5dddlnVnyRH22VTQK02hQPjC1/4QjVCJoflZpnkd+vRRx9tPI5Onjw5fvd3f7f67OSxIWs5m9aS0LK67MXaymMUktcKyANfnnFnh9SWZIjItv9slgHatxzmnbUyWQuStarQ3mimAfhfZvny5VVn2WxyzWa/bLKt1bxAeySMAPwvlBc8zE7p2TSbTbXZMbI2igfaG800AEBROrACAEUJIwBAUcIIAFCUMAIAFCWMAABFCSMAQFHCCABQlDACABQljAAAUdL/ATYN0Gj2Hv9oAAAAAElFTkSuQmCC", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "\n", "plt.bar(x='name',height='price',data=fruit)\n", "# plt.title(\"Fruit Counts\")" ] }, { "cell_type": "code", "execution_count": 56, "id": "1630f084-2d91-4967-bfb6-459307a32702", "metadata": {}, "outputs": [ { "ename": "ValueError", "evalue": "'c' argument must be a color, a sequence of colors, or a sequence of numbers, not array(['Apple', 'Banana', 'Orange', 'Pear'], dtype=object)", "output_type": "error", "traceback": [ "\u001b[31m---------------------------------------------------------------------------\u001b[39m", "\u001b[31mValueError\u001b[39m Traceback (most recent call last)", "\u001b[36mFile \u001b[39m\u001b[32m~/Dropbox/Documents/Teaching/cs503-2026sp/.pixi/envs/default/lib/python3.14/site-packages/matplotlib/axes/_axes.py:4761\u001b[39m, in \u001b[36mAxes._parse_scatter_color_args\u001b[39m\u001b[34m(c, edgecolors, kwargs, xsize, get_next_color_func)\u001b[39m\n\u001b[32m 4760\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m: \u001b[38;5;66;03m# Is 'c' acceptable as PathCollection facecolors?\u001b[39;00m\n\u001b[32m-> \u001b[39m\u001b[32m4761\u001b[39m colors = \u001b[43mmcolors\u001b[49m\u001b[43m.\u001b[49m\u001b[43mto_rgba_array\u001b[49m\u001b[43m(\u001b[49m\u001b[43mc\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 4762\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m (\u001b[38;5;167;01mTypeError\u001b[39;00m, \u001b[38;5;167;01mValueError\u001b[39;00m) \u001b[38;5;28;01mas\u001b[39;00m err:\n", "\u001b[36mFile \u001b[39m\u001b[32m~/Dropbox/Documents/Teaching/cs503-2026sp/.pixi/envs/default/lib/python3.14/site-packages/matplotlib/colors.py:515\u001b[39m, in \u001b[36mto_rgba_array\u001b[39m\u001b[34m(c, alpha)\u001b[39m\n\u001b[32m 514\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m515\u001b[39m rgba = np.array([\u001b[43mto_rgba\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcc\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mfor\u001b[39;00m cc \u001b[38;5;129;01min\u001b[39;00m c])\n\u001b[32m 517\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m alpha \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", "\u001b[36mFile \u001b[39m\u001b[32m~/Dropbox/Documents/Teaching/cs503-2026sp/.pixi/envs/default/lib/python3.14/site-packages/matplotlib/colors.py:317\u001b[39m, in \u001b[36mto_rgba\u001b[39m\u001b[34m(c, alpha)\u001b[39m\n\u001b[32m 316\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m rgba \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m: \u001b[38;5;66;03m# Suppress exception chaining of cache lookup failure.\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m317\u001b[39m rgba = \u001b[43m_to_rgba_no_colorcycle\u001b[49m\u001b[43m(\u001b[49m\u001b[43mc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43malpha\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 318\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n", "\u001b[36mFile \u001b[39m\u001b[32m~/Dropbox/Documents/Teaching/cs503-2026sp/.pixi/envs/default/lib/python3.14/site-packages/matplotlib/colors.py:394\u001b[39m, in \u001b[36m_to_rgba_no_colorcycle\u001b[39m\u001b[34m(c, alpha)\u001b[39m\n\u001b[32m 393\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m c, c, c, alpha \u001b[38;5;28;01mif\u001b[39;00m alpha \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m \u001b[32m1.\u001b[39m\n\u001b[32m--> \u001b[39m\u001b[32m394\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mInvalid RGBA argument: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00morig_c\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[33m\"\u001b[39m)\n\u001b[32m 395\u001b[39m \u001b[38;5;66;03m# turn 2-D array into 1-D array\u001b[39;00m\n", "\u001b[31mValueError\u001b[39m: Invalid RGBA argument: 'Apple'", "\nThe above exception was the direct cause of the following exception:\n", "\u001b[31mValueError\u001b[39m Traceback (most recent call last)", "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[56]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[43mfruit\u001b[49m\u001b[43m.\u001b[49m\u001b[43mplot\u001b[49m\u001b[43m.\u001b[49m\u001b[43mscatter\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m=\u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mprice\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43my\u001b[49m\u001b[43m=\u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mcount\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43mc\u001b[49m\u001b[43m=\u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mname\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n", "\u001b[36mFile \u001b[39m\u001b[32m~/Dropbox/Documents/Teaching/cs503-2026sp/.pixi/envs/default/lib/python3.14/site-packages/pandas/plotting/_core.py:1748\u001b[39m, in \u001b[36mPlotAccessor.scatter\u001b[39m\u001b[34m(self, x, y, s, c, **kwargs)\u001b[39m\n\u001b[32m 1660\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mscatter\u001b[39m(\n\u001b[32m 1661\u001b[39m \u001b[38;5;28mself\u001b[39m,\n\u001b[32m 1662\u001b[39m x: Hashable,\n\u001b[32m (...)\u001b[39m\u001b[32m 1666\u001b[39m **kwargs,\n\u001b[32m 1667\u001b[39m ) -> PlotAccessor:\n\u001b[32m 1668\u001b[39m \u001b[38;5;250m \u001b[39m\u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m 1669\u001b[39m \u001b[33;03m Create a scatter plot with varying marker point size and color.\u001b[39;00m\n\u001b[32m 1670\u001b[39m \n\u001b[32m (...)\u001b[39m\u001b[32m 1746\u001b[39m \u001b[33;03m ... colormap='viridis')\u001b[39;00m\n\u001b[32m 1747\u001b[39m \u001b[33;03m \"\"\"\u001b[39;00m\n\u001b[32m-> \u001b[39m\u001b[32m1748\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mkind\u001b[49m\u001b[43m=\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mscatter\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m=\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m=\u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43ms\u001b[49m\u001b[43m=\u001b[49m\u001b[43ms\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mc\u001b[49m\u001b[43m=\u001b[49m\u001b[43mc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", "\u001b[36mFile \u001b[39m\u001b[32m~/Dropbox/Documents/Teaching/cs503-2026sp/.pixi/envs/default/lib/python3.14/site-packages/pandas/plotting/_core.py:975\u001b[39m, in \u001b[36mPlotAccessor.__call__\u001b[39m\u001b[34m(self, *args, **kwargs)\u001b[39m\n\u001b[32m 973\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m kind \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m._dataframe_kinds:\n\u001b[32m 974\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(data, ABCDataFrame):\n\u001b[32m--> \u001b[39m\u001b[32m975\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mplot_backend\u001b[49m\u001b[43m.\u001b[49m\u001b[43mplot\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m=\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m=\u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkind\u001b[49m\u001b[43m=\u001b[49m\u001b[43mkind\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 976\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 977\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mplot kind \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mkind\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m can only be used for data frames\u001b[39m\u001b[33m\"\u001b[39m)\n", "\u001b[36mFile \u001b[39m\u001b[32m~/Dropbox/Documents/Teaching/cs503-2026sp/.pixi/envs/default/lib/python3.14/site-packages/pandas/plotting/_matplotlib/__init__.py:71\u001b[39m, in \u001b[36mplot\u001b[39m\u001b[34m(data, kind, **kwargs)\u001b[39m\n\u001b[32m 69\u001b[39m kwargs[\u001b[33m\"\u001b[39m\u001b[33max\u001b[39m\u001b[33m\"\u001b[39m] = \u001b[38;5;28mgetattr\u001b[39m(ax, \u001b[33m\"\u001b[39m\u001b[33mleft_ax\u001b[39m\u001b[33m\"\u001b[39m, ax)\n\u001b[32m 70\u001b[39m plot_obj = PLOT_CLASSES[kind](data, **kwargs)\n\u001b[32m---> \u001b[39m\u001b[32m71\u001b[39m \u001b[43mplot_obj\u001b[49m\u001b[43m.\u001b[49m\u001b[43mgenerate\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 72\u001b[39m plot_obj.draw()\n\u001b[32m 73\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m plot_obj.result\n", "\u001b[36mFile \u001b[39m\u001b[32m~/Dropbox/Documents/Teaching/cs503-2026sp/.pixi/envs/default/lib/python3.14/site-packages/pandas/plotting/_matplotlib/core.py:501\u001b[39m, in \u001b[36mMPLPlot.generate\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m 499\u001b[39m \u001b[38;5;28mself\u001b[39m._compute_plot_data()\n\u001b[32m 500\u001b[39m fig = \u001b[38;5;28mself\u001b[39m.fig\n\u001b[32m--> \u001b[39m\u001b[32m501\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_make_plot\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfig\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 502\u001b[39m \u001b[38;5;28mself\u001b[39m._add_table()\n\u001b[32m 503\u001b[39m \u001b[38;5;28mself\u001b[39m._make_legend()\n", "\u001b[36mFile \u001b[39m\u001b[32m~/Dropbox/Documents/Teaching/cs503-2026sp/.pixi/envs/default/lib/python3.14/site-packages/pandas/plotting/_matplotlib/core.py:1351\u001b[39m, in \u001b[36mScatterPlot._make_plot\u001b[39m\u001b[34m(self, fig)\u001b[39m\n\u001b[32m 1349\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 1350\u001b[39m label = \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m-> \u001b[39m\u001b[32m1351\u001b[39m scatter = \u001b[43max\u001b[49m\u001b[43m.\u001b[49m\u001b[43mscatter\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 1352\u001b[39m \u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m[\u001b[49m\u001b[43mx\u001b[49m\u001b[43m]\u001b[49m\u001b[43m.\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1353\u001b[39m \u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m[\u001b[49m\u001b[43my\u001b[49m\u001b[43m]\u001b[49m\u001b[43m.\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1354\u001b[39m \u001b[43m \u001b[49m\u001b[43mc\u001b[49m\u001b[43m=\u001b[49m\u001b[43mc_values\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1355\u001b[39m \u001b[43m \u001b[49m\u001b[43mlabel\u001b[49m\u001b[43m=\u001b[49m\u001b[43mlabel\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1356\u001b[39m \u001b[43m \u001b[49m\u001b[43mcmap\u001b[49m\u001b[43m=\u001b[49m\u001b[43mcmap\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1357\u001b[39m \u001b[43m \u001b[49m\u001b[43mnorm\u001b[49m\u001b[43m=\u001b[49m\u001b[43mnorm\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1358\u001b[39m \u001b[43m \u001b[49m\u001b[43ms\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43ms\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1359\u001b[39m \u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mkwds\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1360\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 1361\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m cb:\n\u001b[32m 1362\u001b[39m cbar_label = c \u001b[38;5;28;01mif\u001b[39;00m c_is_column \u001b[38;5;28;01melse\u001b[39;00m \u001b[33m\"\u001b[39m\u001b[33m\"\u001b[39m\n", "\u001b[36mFile \u001b[39m\u001b[32m~/Dropbox/Documents/Teaching/cs503-2026sp/.pixi/envs/default/lib/python3.14/site-packages/matplotlib/_api/deprecation.py:453\u001b[39m, in \u001b[36mmake_keyword_only..wrapper\u001b[39m\u001b[34m(*args, **kwargs)\u001b[39m\n\u001b[32m 447\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(args) > name_idx:\n\u001b[32m 448\u001b[39m warn_deprecated(\n\u001b[32m 449\u001b[39m since, message=\u001b[33m\"\u001b[39m\u001b[33mPassing the \u001b[39m\u001b[38;5;132;01m%(name)s\u001b[39;00m\u001b[33m \u001b[39m\u001b[38;5;132;01m%(obj_type)s\u001b[39;00m\u001b[33m \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 450\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mpositionally is deprecated since Matplotlib \u001b[39m\u001b[38;5;132;01m%(since)s\u001b[39;00m\u001b[33m; the \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 451\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mparameter will become keyword-only in \u001b[39m\u001b[38;5;132;01m%(removal)s\u001b[39;00m\u001b[33m.\u001b[39m\u001b[33m\"\u001b[39m,\n\u001b[32m 452\u001b[39m name=name, obj_type=\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mparameter of \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfunc.\u001b[34m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m()\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m--> \u001b[39m\u001b[32m453\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", "\u001b[36mFile \u001b[39m\u001b[32m~/Dropbox/Documents/Teaching/cs503-2026sp/.pixi/envs/default/lib/python3.14/site-packages/matplotlib/__init__.py:1524\u001b[39m, in \u001b[36m_preprocess_data..inner\u001b[39m\u001b[34m(ax, data, *args, **kwargs)\u001b[39m\n\u001b[32m 1521\u001b[39m \u001b[38;5;129m@functools\u001b[39m.wraps(func)\n\u001b[32m 1522\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34minner\u001b[39m(ax, *args, data=\u001b[38;5;28;01mNone\u001b[39;00m, **kwargs):\n\u001b[32m 1523\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m data \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m-> \u001b[39m\u001b[32m1524\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 1525\u001b[39m \u001b[43m \u001b[49m\u001b[43max\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1526\u001b[39m \u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[38;5;28;43mmap\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mcbook\u001b[49m\u001b[43m.\u001b[49m\u001b[43msanitize_sequence\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1527\u001b[39m \u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43m{\u001b[49m\u001b[43mk\u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mcbook\u001b[49m\u001b[43m.\u001b[49m\u001b[43msanitize_sequence\u001b[49m\u001b[43m(\u001b[49m\u001b[43mv\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mk\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mv\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m.\u001b[49m\u001b[43mitems\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 1529\u001b[39m bound = new_sig.bind(ax, *args, **kwargs)\n\u001b[32m 1530\u001b[39m auto_label = (bound.arguments.get(label_namer)\n\u001b[32m 1531\u001b[39m \u001b[38;5;129;01mor\u001b[39;00m bound.kwargs.get(label_namer))\n", "\u001b[36mFile \u001b[39m\u001b[32m~/Dropbox/Documents/Teaching/cs503-2026sp/.pixi/envs/default/lib/python3.14/site-packages/matplotlib/axes/_axes.py:4954\u001b[39m, in \u001b[36mAxes.scatter\u001b[39m\u001b[34m(self, x, y, s, c, marker, cmap, norm, vmin, vmax, alpha, linewidths, edgecolors, colorizer, plotnonfinite, **kwargs)\u001b[39m\n\u001b[32m 4951\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m edgecolors \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m 4952\u001b[39m orig_edgecolor = kwargs.get(\u001b[33m'\u001b[39m\u001b[33medgecolor\u001b[39m\u001b[33m'\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[32m 4953\u001b[39m c, colors, edgecolors = \\\n\u001b[32m-> \u001b[39m\u001b[32m4954\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_parse_scatter_color_args\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 4955\u001b[39m \u001b[43m \u001b[49m\u001b[43mc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43medgecolors\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m.\u001b[49m\u001b[43msize\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 4956\u001b[39m \u001b[43m \u001b[49m\u001b[43mget_next_color_func\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_get_patches_for_fill\u001b[49m\u001b[43m.\u001b[49m\u001b[43mget_next_color\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 4958\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m plotnonfinite \u001b[38;5;129;01mand\u001b[39;00m colors \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m 4959\u001b[39m c = np.ma.masked_invalid(c)\n", "\u001b[36mFile \u001b[39m\u001b[32m~/Dropbox/Documents/Teaching/cs503-2026sp/.pixi/envs/default/lib/python3.14/site-packages/matplotlib/axes/_axes.py:4770\u001b[39m, in \u001b[36mAxes._parse_scatter_color_args\u001b[39m\u001b[34m(c, edgecolors, kwargs, xsize, get_next_color_func)\u001b[39m\n\u001b[32m 4767\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m invalid_shape_exception(c.size, xsize) \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01merr\u001b[39;00m\n\u001b[32m 4768\u001b[39m \u001b[38;5;66;03m# Both the mapping *and* the RGBA conversion failed: pretty\u001b[39;00m\n\u001b[32m 4769\u001b[39m \u001b[38;5;66;03m# severe failure => one may appreciate a verbose feedback.\u001b[39;00m\n\u001b[32m-> \u001b[39m\u001b[32m4770\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[32m 4771\u001b[39m \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33m'\u001b[39m\u001b[33mc\u001b[39m\u001b[33m'\u001b[39m\u001b[33m argument must be a color, a sequence of colors, \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 4772\u001b[39m \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mor a sequence of numbers, not \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mc\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[33m\"\u001b[39m) \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01merr\u001b[39;00m\n\u001b[32m 4773\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 4774\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(colors) \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m (\u001b[32m0\u001b[39m, \u001b[32m1\u001b[39m, xsize):\n\u001b[32m 4775\u001b[39m \u001b[38;5;66;03m# NB: remember that a single color is also acceptable.\u001b[39;00m\n\u001b[32m 4776\u001b[39m \u001b[38;5;66;03m# Besides *colors* will be an empty array if c == 'none'.\u001b[39;00m\n", "\u001b[31mValueError\u001b[39m: 'c' argument must be a color, a sequence of colors, or a sequence of numbers, not array(['Apple', 'Banana', 'Orange', 'Pear'], dtype=object)" ] }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fruit.plot.scatter(x='price',y='count',c='name')" ] }, { "cell_type": "code", "execution_count": 57, "id": "b060121c-aa26-4a13-96b6-bf25a27ef987", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 1.0, 'Fruit Prices')" ] }, "execution_count": 57, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "colors = {'Apple': 'red','Orange': 'orange','Banana': 'yellow','Pear': 'green'}\n", "ax = fruit.plot.scatter(x='price',y='count', c=fruit['name'].map(colors), label=fruit['name'].tolist(), legend=True)\n", "ax.set_title('Fruit Prices')" ] }, { "cell_type": "code", "execution_count": 58, "id": "29859e4d-cae2-46cc-a397-d26a544a8f31", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 58, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "colors = {'Apple': 'red','Orange': 'orange','Banana': 'yellow','Pear': 'green'}\n", "for name, color in colors.items():\n", " plt.scatter(x='price',y='count', c=color, label=name, data=fruit[fruit.name == name])\n", "plt.title('Fruit Prices')\n", "plt.legend()" ] }, { "cell_type": "code", "execution_count": 59, "id": "a44b63bd-4f20-4103-8d66-1a9e9c9ca066", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 59, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# may need to install seaborn\n", "# %conda install -c conda-forge seaborn\n", "\n", "import seaborn as sns\n", "sns.scatterplot(x='price',y='count',hue='name', data=fruit)" ] }, { "cell_type": "markdown", "id": "90dd4628-092e-45ae-a691-c608af7fcaf9", "metadata": {}, "source": [ "### Concatenation, Layering, and Repetition" ] }, { "cell_type": "code", "execution_count": 60, "id": "86d2a9a5-a073-4f62-a4c5-da93ff904e24", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "
\n", "" ], "text/plain": [ "alt.Chart(...)" ] }, "execution_count": 60, "metadata": {}, "output_type": "execute_result" } ], "source": [ "c1 = alt.Chart(data).mark_point(filled=True, stroke='black',strokeWidth=1).encode(\n", " x='age',\n", " y='weight',\n", " color='zoo_area:N',\n", " size='num_scoops'\n", ")" ] }, { "cell_type": "code", "execution_count": 61, "id": "5df90b96-e131-4e2d-90d1-1d4d0ec3d1b7", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "
\n", "" ], "text/plain": [ "alt.Chart(...)" ] }, "execution_count": 61, "metadata": {}, "output_type": "execute_result" } ], "source": [ "c2 = alt.Chart(data).mark_bar().encode(\n", " x='zoo_area:N',\n", " y='count()'\n", ")" ] }, { "cell_type": "code", "execution_count": 62, "id": "43a11843-5c4b-44ab-89f8-933e5bdd87a2", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "
\n", "" ], "text/plain": [ "alt.HConcatChart(...)" ] }, "execution_count": 62, "metadata": {}, "output_type": "execute_result" } ], "source": [ "c1 | c2" ] }, { "cell_type": "code", "execution_count": 63, "id": "8096a5a5-0e43-48eb-b7c3-24dce3a0d7d5", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "
\n", "" ], "text/plain": [ "alt.VConcatChart(...)" ] }, "execution_count": 63, "metadata": {}, "output_type": "execute_result" } ], "source": [ "c1 & c2" ] }, { "cell_type": "code", "execution_count": 64, "id": "7467cc77-a09a-4335-813b-61e03ee49a3d", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "
\n", "" ], "text/plain": [ "alt.Chart(...)" ] }, "execution_count": 64, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data = pd.DataFrame({\n", " 'x': [1,3,4,6,10],\n", " 'y': [1,5,2,7,3]\n", "})\n", "\n", "lines = alt.Chart(data).mark_line().encode(\n", " x='x',\n", " y='y'\n", ")\n", "\n", "dots = alt.Chart(data).mark_point(size=150, shape='triangle', color='red',filled=True).encode(\n", " x='x',\n", " y='y'\n", ")\n", "\n", "lines" ] }, { "cell_type": "code", "execution_count": 65, "id": "e97a67a4-7572-4553-990f-2ebe53ae9aa9", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "
\n", "" ], "text/plain": [ "alt.Chart(...)" ] }, "execution_count": 65, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dots" ] }, { "cell_type": "code", "execution_count": 66, "id": "1503e94f-ff58-4032-a42f-1c21829a4ef8", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "
\n", "" ], "text/plain": [ "alt.LayerChart(...)" ] }, "execution_count": 66, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lines + dots" ] }, { "cell_type": "code", "execution_count": 69, "id": "4b01d126-88c0-4137-bc0d-f61768ea9f42", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "
\n", "" ], "text/plain": [ "alt.Chart(...)" ] }, "execution_count": 69, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import altair as alt\n", "\n", "penguins = 'https://raw.githubusercontent.com/vega/vega-datasets/master/data/penguins.json'\n", "\n", "alt.Chart(penguins).mark_point().encode(\n", " alt.X(\"Beak Length (mm):Q\", scale=alt.Scale(zero=False)),\n", " alt.Y(\"Beak Depth (mm):Q\", scale=alt.Scale(zero=False)),\n", " color='Species:N'\n", ")" ] }, { "cell_type": "code", "execution_count": 70, "id": "820362a1-f88b-43ad-b166-b874fb1f0649", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "
\n", "" ], "text/plain": [ "alt.Chart(...)" ] }, "execution_count": 70, "metadata": {}, "output_type": "execute_result" } ], "source": [ "alt.Chart(penguins).mark_point().encode(\n", " alt.X(\"Beak Length (mm):Q\", scale=alt.Scale(zero=False)),\n", " alt.Y(\"Beak Depth (mm):Q\", scale=alt.Scale(zero=False)),\n", " color='Species:N'\n", ").interactive()" ] }, { "cell_type": "code", "execution_count": 71, "id": "0551e8c5-9723-44cd-bb0f-c12cec5cb735", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "
\n", "" ], "text/plain": [ "alt.RepeatChart(...)" ] }, "execution_count": 71, "metadata": {}, "output_type": "execute_result" } ], "source": [ "penguins = 'https://raw.githubusercontent.com/vega/vega-datasets/master/data/penguins.json'\n", "\n", "alt.Chart(penguins).mark_point().encode(\n", " alt.X(alt.repeat(\"row\"), type='quantitative', scale=alt.Scale(zero=False)),\n", " alt.Y(alt.repeat(\"column\"), type='quantitative', scale=alt.Scale(zero=False)),\n", " color='Species:N'\n", ").properties(\n", " width=150,\n", " height=150\n", ").repeat(\n", " row=['Beak Length (mm)', 'Beak Depth (mm)', 'Flipper Length (mm)'],\n", " column=['Beak Length (mm)', 'Beak Depth (mm)', 'Flipper Length (mm)']\n", ")" ] }, { "cell_type": "code", "execution_count": null, "id": "43cd5a91-ead5-4d0d-849a-892150f44c30", "metadata": {}, "outputs": [], "source": [ "# %conda install -c conda-forge vega_datasets" ] }, { "cell_type": "code", "execution_count": 72, "id": "1db340a2-cb35-4cd2-9929-1d062baf67b9", "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "
\n", "" ], "text/plain": [ "alt.Chart(...)" ] }, "execution_count": 72, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import altair as alt\n", "from vega_datasets import data\n", "\n", "states = alt.topo_feature(data.us_10m.url, 'states')\n", "source = data.income.url\n", "\n", "alt.Chart(source).mark_geoshape().encode(\n", " shape='geo:G',\n", " color='pct:Q',\n", " tooltip=['name:N', 'pct:Q'],\n", " facet=alt.Facet('group:N', columns=2),\n", ").transform_lookup(\n", " lookup='id',\n", " from_=alt.LookupData(data=states, key='id'),\n", " as_='geo'\n", ").properties(\n", " width=300,\n", " height=175,\n", ").project(\n", " type='albersUsa'\n", ")" ] }, { "cell_type": "code", "execution_count": null, "id": "49381f21-fb3b-478b-a793-a2fd9b986584", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.14.2" } }, "nbformat": 4, "nbformat_minor": 5 }