Week 3, Wed, 4/16

import seaborn as sns
df = sns.load_dataset("penguins")

df

	species	island	bill_length_mm	bill_depth_mm	flipper_length_mm	body_mass_g	sex
0	Adelie	Torgersen	39.1	18.7	181.0	3750.0	Male
1	Adelie	Torgersen	39.5	17.4	186.0	3800.0	Female
2	Adelie	Torgersen	40.3	18.0	195.0	3250.0	Female
3	Adelie	Torgersen	NaN	NaN	NaN	NaN	NaN
4	Adelie	Torgersen	36.7	19.3	193.0	3450.0	Female
...	...	...	...	...	...	...	...
339	Gentoo	Biscoe	NaN	NaN	NaN	NaN	NaN
340	Gentoo	Biscoe	46.8	14.3	215.0	4850.0	Female
341	Gentoo	Biscoe	50.4	15.7	222.0	5750.0	Male
342	Gentoo	Biscoe	45.2	14.8	212.0	5200.0	Female
343	Gentoo	Biscoe	49.9	16.1	213.0	5400.0	Male

344 rows × 7 columns

df['body_mass_g2'] = df['body_mass_g'].apply(lambda x: x/1000)

df

	species	island	bill_length_mm	bill_depth_mm	flipper_length_mm	body_mass_g	sex	body_mass_g2
0	Adelie	Torgersen	39.1	18.7	181.0	3750.0	Male	3.75
1	Adelie	Torgersen	39.5	17.4	186.0	3800.0	Female	3.80
2	Adelie	Torgersen	40.3	18.0	195.0	3250.0	Female	3.25
3	Adelie	Torgersen	NaN	NaN	NaN	NaN	NaN	NaN
4	Adelie	Torgersen	36.7	19.3	193.0	3450.0	Female	3.45
...	...	...	...	...	...	...	...	...
339	Gentoo	Biscoe	NaN	NaN	NaN	NaN	NaN	NaN
340	Gentoo	Biscoe	46.8	14.3	215.0	4850.0	Female	4.85
341	Gentoo	Biscoe	50.4	15.7	222.0	5750.0	Male	5.75
342	Gentoo	Biscoe	45.2	14.8	212.0	5200.0	Female	5.20
343	Gentoo	Biscoe	49.9	16.1	213.0	5400.0	Male	5.40

344 rows × 8 columns

df.rename(columns={'body_mass_g2':'body_mass_kg','sex':'gender'},inplace=True)

df

	species	island	bill_length_mm	bill_depth_mm	flipper_length_mm	body_mass_g	gender	body_mass_kg
0	Adelie	Torgersen	39.1	18.7	181.0	3750.0	Male	3.75
1	Adelie	Torgersen	39.5	17.4	186.0	3800.0	Female	3.80
2	Adelie	Torgersen	40.3	18.0	195.0	3250.0	Female	3.25
3	Adelie	Torgersen	NaN	NaN	NaN	NaN	NaN	NaN
4	Adelie	Torgersen	36.7	19.3	193.0	3450.0	Female	3.45
...	...	...	...	...	...	...	...	...
339	Gentoo	Biscoe	NaN	NaN	NaN	NaN	NaN	NaN
340	Gentoo	Biscoe	46.8	14.3	215.0	4850.0	Female	4.85
341	Gentoo	Biscoe	50.4	15.7	222.0	5750.0	Male	5.75
342	Gentoo	Biscoe	45.2	14.8	212.0	5200.0	Female	5.20
343	Gentoo	Biscoe	49.9	16.1	213.0	5400.0	Male	5.40

344 rows × 8 columns

idx = df['body_mass_g'].idxmax()
idx

237

df.loc[idx, 'bill_length_mm']

49.2

df[(df.island=='Dream') & (df.body_mass_g>4000)].shape

(28, 8)

import seaborn as sns
df = sns.load_dataset("penguins")

df

	species	island	bill_length_mm	bill_depth_mm	flipper_length_mm	body_mass_g	sex
0	Adelie	Torgersen	39.1	18.7	181.0	3750.0	Male
1	Adelie	Torgersen	39.5	17.4	186.0	3800.0	Female
2	Adelie	Torgersen	40.3	18.0	195.0	3250.0	Female
3	Adelie	Torgersen	NaN	NaN	NaN	NaN	NaN
4	Adelie	Torgersen	36.7	19.3	193.0	3450.0	Female
...	...	...	...	...	...	...	...
339	Gentoo	Biscoe	NaN	NaN	NaN	NaN	NaN
340	Gentoo	Biscoe	46.8	14.3	215.0	4850.0	Female
341	Gentoo	Biscoe	50.4	15.7	222.0	5750.0	Male
342	Gentoo	Biscoe	45.2	14.8	212.0	5200.0	Female
343	Gentoo	Biscoe	49.9	16.1	213.0	5400.0	Male

344 rows × 7 columns

df_clean = df.dropna()

df_clean

	species	island	bill_length_mm	bill_depth_mm	flipper_length_mm	body_mass_g	sex
0	Adelie	Torgersen	39.1	18.7	181.0	3750.0	Male
1	Adelie	Torgersen	39.5	17.4	186.0	3800.0	Female
2	Adelie	Torgersen	40.3	18.0	195.0	3250.0	Female
4	Adelie	Torgersen	36.7	19.3	193.0	3450.0	Female
5	Adelie	Torgersen	39.3	20.6	190.0	3650.0	Male
...	...	...	...	...	...	...	...
338	Gentoo	Biscoe	47.2	13.7	214.0	4925.0	Female
340	Gentoo	Biscoe	46.8	14.3	215.0	4850.0	Female
341	Gentoo	Biscoe	50.4	15.7	222.0	5750.0	Male
342	Gentoo	Biscoe	45.2	14.8	212.0	5200.0	Female
343	Gentoo	Biscoe	49.9	16.1	213.0	5400.0	Male

333 rows × 7 columns

df[df.isna().any(axis=1)]

	species	island	bill_length_mm	bill_depth_mm	flipper_length_mm	body_mass_g	sex
3	Adelie	Torgersen	NaN	NaN	NaN	NaN	NaN
8	Adelie	Torgersen	34.1	18.1	193.0	3475.0	NaN
9	Adelie	Torgersen	42.0	20.2	190.0	4250.0	NaN
10	Adelie	Torgersen	37.8	17.1	186.0	3300.0	NaN
11	Adelie	Torgersen	37.8	17.3	180.0	3700.0	NaN
47	Adelie	Dream	37.5	18.9	179.0	2975.0	NaN
246	Gentoo	Biscoe	44.5	14.3	216.0	4100.0	NaN
286	Gentoo	Biscoe	46.2	14.4	214.0	4650.0	NaN
324	Gentoo	Biscoe	47.3	13.8	216.0	4725.0	NaN
336	Gentoo	Biscoe	44.5	15.7	217.0	4875.0	NaN
339	Gentoo	Biscoe	NaN	NaN	NaN	NaN	NaN

df.groupby('species')[['bill_length_mm','bill_depth_mm']].mean()

	bill_length_mm	bill_depth_mm
species
Adelie	38.791391	18.346358
Chinstrap	48.833824	18.420588
Gentoo	47.504878	14.982114

df.bill_length_mm.corr(df.bill_depth_mm)

-0.23505287035553263

sns.scatterplot(
    data = df,
    x = "bill_length_mm",
    y = "bill_depth_mm",
)

<Axes: xlabel='bill_length_mm', ylabel='bill_depth_mm'>

species = df.species.unique()
species

array(['Adelie', 'Chinstrap', 'Gentoo'], dtype=object)

for s in species:
    df_s =df[df.species==s]
    corr = df_s.bill_length_mm.corr(df_s.bill_depth_mm)
    print(f"{s}:{corr:.2f}")

Adelie:0.39
Chinstrap:0.65
Gentoo:0.64

sns.scatterplot(
    data = df,
    x = "bill_length_mm",
    y = "bill_depth_mm",
    hue = 'species'
)

<Axes: xlabel='bill_length_mm', ylabel='bill_depth_mm'>

import altair as alt
alt.Chart(df).mark_circle().encode(
    x = alt.X("bill_length_mm",scale=alt.Scale(domain=(30, 60))),
    y = alt.X("bill_depth_mm",scale=alt.Scale(domain=(12, 22))),
    color = "species",
    tooltip = ["species", "bill_length_mm", "bill_depth_mm", "island", "sex"]
)

brush = alt.selection_interval()

c1 = alt.Chart(df).mark_point().encode(
    x = alt.X("bill_length_mm",scale=alt.Scale(domain=(30, 60))),
    y = alt.X("bill_depth_mm",scale=alt.Scale(domain=(12, 22))),
    color = "species:N",
    tooltip = ["species", "bill_length_mm", "bill_depth_mm", "island", "sex"]
).add_params(brush)

c2 = alt.Chart(df).mark_bar().encode(
    x="species:N",
    y="count()"
).transform_filter(brush)

c1|c2

sns.displot(df, x="flipper_length_mm", hue="species")

<seaborn.axisgrid.FacetGrid at 0x3214c1250>

sns.violinplot(data=df, x="species", y="flipper_length_mm",hue="sex")

<Axes: xlabel='species', ylabel='flipper_length_mm'>

sns.pairplot(df, diag_kind="hist", hue = 'species')

<seaborn.axisgrid.PairGrid at 0x323548f50>