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pandas_groupby_statistics.py
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pandas_groupby_statistics.py
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import pandas as pd
import seaborn as sns
import numpy as np
df = sns.load_dataset("iris")
print(df.shape)
# (150, 5)
print(df.head(5))
# sepal_length sepal_width petal_length petal_width species
# 0 5.1 3.5 1.4 0.2 setosa
# 1 4.9 3.0 1.4 0.2 setosa
# 2 4.7 3.2 1.3 0.2 setosa
# 3 4.6 3.1 1.5 0.2 setosa
# 4 5.0 3.6 1.4 0.2 setosa
df.columns = ['sl', 'sw', 'pl', 'pw', 'species']
print(df.head(5))
# sl sw pl pw species
# 0 5.1 3.5 1.4 0.2 setosa
# 1 4.9 3.0 1.4 0.2 setosa
# 2 4.7 3.2 1.3 0.2 setosa
# 3 4.6 3.1 1.5 0.2 setosa
# 4 5.0 3.6 1.4 0.2 setosa
grouped = df.groupby('species')
print(grouped)
# <pandas.core.groupby.groupby.DataFrameGroupBy object at 0x10c69f6a0>
print(type(grouped))
# <class 'pandas.core.groupby.groupby.DataFrameGroupBy'>
print(grouped.size())
# species
# setosa 50
# versicolor 50
# virginica 50
# dtype: int64
print(grouped.mean())
# sl sw pl pw
# species
# setosa 5.006 3.428 1.462 0.246
# versicolor 5.936 2.770 4.260 1.326
# virginica 6.588 2.974 5.552 2.026
print(grouped.min())
# sl sw pl pw
# species
# setosa 4.3 2.3 1.0 0.1
# versicolor 4.9 2.0 3.0 1.0
# virginica 4.9 2.2 4.5 1.4
print(grouped.max())
# sl sw pl pw
# species
# setosa 5.8 4.4 1.9 0.6
# versicolor 7.0 3.4 5.1 1.8
# virginica 7.9 3.8 6.9 2.5
print(grouped.sum())
# sl sw pl pw
# species
# setosa 250.3 171.4 73.1 12.3
# versicolor 296.8 138.5 213.0 66.3
# virginica 329.4 148.7 277.6 101.3
print(type(grouped.mean()))
# <class 'pandas.core.frame.DataFrame'>
print(grouped.agg(min))
# sl sw pl pw
# species
# setosa 4.3 2.3 1.0 0.1
# versicolor 4.9 2.0 3.0 1.0
# virginica 4.9 2.2 4.5 1.4
print(grouped.agg('max'))
# sl sw pl pw
# species
# setosa 5.8 4.4 1.9 0.6
# versicolor 7.0 3.4 5.1 1.8
# virginica 7.9 3.8 6.9 2.5
# print(grouped.agg(mean))
# NameError: name 'mean' is not defined
print(grouped.agg(np.mean))
# sl sw pl pw
# species
# setosa 5.006 3.428 1.462 0.246
# versicolor 5.936 2.770 4.260 1.326
# virginica 6.588 2.974 5.552 2.026
print(grouped.agg('mean'))
# sl sw pl pw
# species
# setosa 5.006 3.428 1.462 0.246
# versicolor 5.936 2.770 4.260 1.326
# virginica 6.588 2.974 5.552 2.026
print(grouped.agg([min, 'max']))
# sl sw pl pw
# min max min max min max min max
# species
# setosa 4.3 5.8 2.3 4.4 1.0 1.9 0.1 0.6
# versicolor 4.9 7.0 2.0 3.4 3.0 5.1 1.0 1.8
# virginica 4.9 7.9 2.2 3.8 4.5 6.9 1.4 2.5
print(grouped.agg({'sl': min, 'sw': max, 'pl': np.mean, 'pw': 'mean'}))
# sl sw pl pw
# species
# setosa 4.3 4.4 1.462 0.246
# versicolor 4.9 3.4 4.260 1.326
# virginica 4.9 3.8 5.552 2.026
print(grouped.agg(lambda x: max(x) - min(x)))
# sl sw pl pw
# species
# setosa 1.5 2.1 0.9 0.5
# versicolor 2.1 1.4 2.1 0.8
# virginica 3.0 1.6 2.4 1.1
print(grouped.agg(lambda x: type(x))['sl'])
# species
# setosa <class 'pandas.core.series.Series'>
# versicolor <class 'pandas.core.series.Series'>
# virginica <class 'pandas.core.series.Series'>
# Name: sl, dtype: object
# print(grouped.agg(lambda x: x + 1))
# Exception: Must produce aggregated value
print(grouped.describe()['sl'])
# count mean std min 25% 50% 75% max
# species
# setosa 50.0 5.006 0.352490 4.3 4.800 5.0 5.2 5.8
# versicolor 50.0 5.936 0.516171 4.9 5.600 5.9 6.3 7.0
# virginica 50.0 6.588 0.635880 4.9 6.225 6.5 6.9 7.9
print(type(grouped.max()))
# <class 'pandas.core.frame.DataFrame'>
ax = grouped.max().plot.bar(rot=0)
fig = ax.get_figure()
fig.savefig('data/dst/iris_pandas_groupby_max.jpg')