/
pandas_idxmax_idxmin.py
219 lines (173 loc) · 3.41 KB
/
pandas_idxmax_idxmin.py
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import pandas as pd
df = pd.DataFrame({'col1': [0, 3, 2, 3], 'col2': [4, 0, 2, 1]},
index=['a', 'b', 'c', 'd'])
print(df)
# col1 col2
# a 0 4
# b 3 0
# c 2 2
# d 3 1
print(df['col1'])
# a 0
# b 3
# c 2
# d 3
# Name: col1, dtype: int64
print(type(df['col1']))
# <class 'pandas.core.series.Series'>
print(df['col1'].max())
# 3
print(df['col1'].min())
# 0
print(df.max())
# col1 3
# col2 4
# dtype: int64
print(df.min())
# col1 0
# col2 0
# dtype: int64
print(df.max(axis=1))
# a 4
# b 3
# c 2
# d 3
# dtype: int64
print(df.min(axis=1))
# a 0
# b 0
# c 2
# d 1
# dtype: int64
print(type(df.max()))
# <class 'pandas.core.series.Series'>
print(df['col1'].idxmax())
# b
print(df['col1'].idxmin())
# a
print(df['col1'] == df['col1'].max())
# a False
# b True
# c False
# d True
# Name: col1, dtype: bool
print(df['col1'][df['col1'] == df['col1'].max()])
# b 3
# d 3
# Name: col1, dtype: int64
print(df['col1'][df['col1'] == df['col1'].max()].index)
# Index(['b', 'd'], dtype='object')
print(df['col1'][df['col1'] == df['col1'].max()].index.values)
# ['b' 'd']
print(type(df['col1'][df['col1'] == df['col1'].max()].index.values))
# <class 'numpy.ndarray'>
print(list(df['col1'][df['col1'] == df['col1'].max()].index))
# ['b', 'd']
print(type(list(df['col1'][df['col1'] == df['col1'].max()].index)))
# <class 'list'>
print(df['col1'][df['col1'] == df['col1'].min()].index.values)
# ['a']
print(df.loc['a'])
# col1 0
# col2 4
# Name: a, dtype: int64
print(df.loc['a'].idxmax())
# col2
print(df.loc['a'].idxmin())
# col1
print(df.idxmax())
# col1 b
# col2 a
# dtype: object
print(df.idxmin())
# col1 a
# col2 b
# dtype: object
print(df.apply(lambda x: list(x[x == x.max()].index)))
# col1 [b, d]
# col2 [a]
# dtype: object
print(df.apply(lambda x: list(x[x == x.min()].index)))
# col1 [a]
# col2 [b]
# dtype: object
print(df.idxmax(axis=1))
# a col2
# b col1
# c col1
# d col1
# dtype: object
print(df.idxmin(axis=1))
# a col1
# b col2
# c col1
# d col2
# dtype: object
print(df.apply(lambda x: list(x[x == x.max()].index), axis=1))
# a [col2]
# b [col1]
# c [col1, col2]
# d [col1]
# dtype: object
print(df.apply(lambda x: list(x[x == x.min()].index), axis=1))
# a [col1]
# b [col2]
# c [col1, col2]
# d [col2]
# dtype: object
df_nan = df.copy()
df_nan.at['b'] = pd.np.nan
print(df_nan)
# col1 col2
# a 0.0 4.0
# b NaN NaN
# c 2.0 2.0
# d 3.0 1.0
print(df_nan.idxmax())
# col1 d
# col2 a
# dtype: object
print(df_nan.idxmin())
# col1 a
# col2 d
# dtype: object
print(df_nan.idxmax(axis=1))
# a col2
# b NaN
# c col1
# d col1
# dtype: object
print(df_nan.idxmin(axis=1))
# a col1
# b NaN
# c col1
# d col2
# dtype: object
print(df_nan.idxmax(skipna=False))
# col1 NaN
# col2 NaN
# dtype: float64
print(df_nan.idxmin(skipna=False))
# col1 NaN
# col2 NaN
# dtype: float64
print(df_nan.idxmax(axis=1, skipna=False))
# a col2
# b NaN
# c col1
# d col1
# dtype: object
print(df_nan.idxmin(axis=1, skipna=False))
# a col1
# b NaN
# c col1
# d col2
# dtype: object
print(df_nan['col1'].idxmax())
# d
print(df_nan['col1'].idxmin())
# a
print(df_nan['col1'].idxmax(skipna=False))
# nan
print(df_nan['col1'].idxmin(skipna=False))
# nan