Python 列的最大值和最小值之间的差异
我有一个包含2000多列的熊猫数据框架。所有列都有数值。我想找出每列的最小值和最大值之间的差异。然后我想过滤差异最大的前10列Python 列的最大值和最小值之间的差异,python,pandas,Python,Pandas,我有一个包含2000多列的熊猫数据框架。所有列都有数值。我想找出每列的最小值和最大值之间的差异。然后我想过滤差异最大的前10列 Col1 Col2 Col3 ..... Col2500 4 1 3 ..... 6 7 5 10 ..... 17 1 22 4 ..... 2 我尝试了一些选择,但没有一个有效! 请提出解决方案。这将在系列中为您提供结果: import pandas as pd import numpy as n
Col1 Col2 Col3 ..... Col2500
4 1 3 ..... 6
7 5 10 ..... 17
1 22 4 ..... 2
我尝试了一些选择,但没有一个有效!
请提出解决方案。这将在
系列中为您提供结果:
import pandas as pd
import numpy as np
#sample data
df = pd.DataFrame(np.random.randint(0,100,size=(100, 4)), columns=list('ABCD'))
#transposing data so columns are now rows and column names are indices
df = df.transpose()
#Calculation of Max - Min per row
df['dif'] = df.max(axis=1) - df.min(axis = 1)
#Number of results at the end (10 in your case)
TOP_N = 2
#Resetting the index to get column names and sorting by difference high to low
result = df.reset_index().rename(columns={'index':'ColumnName'})[['ColumnName','dif']].sort_values(by=['dif'],ascending=[False]).head(TOP_N)
print(result)
df.T.apply(lambda x: x.max() - x.min(), axis=1).nlargest(10)
例如:
df
Col1 Col2 Col3 Col2500
0 4 1 3 6
1 7 5 10 17
2 1 22 4 2
df.T.apply(lambda x: x.max() - x.min(), axis=1).nlargest(3)
Col2 21
Col2500 15
Col3 7
dtype: int64
>>> df.values
array([[ 0, 12, 42],
[ 1, 13, 21],
[ 12, 1, 30],
[ 3, 45, -39],
[ 4, 1, 38]])
>>> diff = df.max() - df.min()
>>>
>>> diff.sort_values(ascending=False)
T3 81
T2 44
T1 12
dtype: int64
>>> diff.sort_values()
T1 12
T2 44
T3 81
dtype: int64
>>>
或者只是:
(df.max() - df.min()).nlargest(10)
这将在系列中为您提供结果:
df.T.apply(lambda x: x.max() - x.min(), axis=1).nlargest(10)
例如:
df
Col1 Col2 Col3 Col2500
0 4 1 3 6
1 7 5 10 17
2 1 22 4 2
df.T.apply(lambda x: x.max() - x.min(), axis=1).nlargest(3)
Col2 21
Col2500 15
Col3 7
dtype: int64
>>> df.values
array([[ 0, 12, 42],
[ 1, 13, 21],
[ 12, 1, 30],
[ 3, 45, -39],
[ 4, 1, 38]])
>>> diff = df.max() - df.min()
>>>
>>> diff.sort_values(ascending=False)
T3 81
T2 44
T1 12
dtype: int64
>>> diff.sort_values()
T1 12
T2 44
T3 81
dtype: int64
>>>
或者只是:
(df.max() - df.min()).nlargest(10)
这是我的解决办法
>>> data = {'Col1':[4,7,1],'Col2':[1,5,22], 'Col3':[3,10,4], 'Col2500':[6,17,2]}
>>> df = pd.DataFrame(data)
>>> df
Col1 Col2 Col3 Col2500
0 4 1 3 6
1 7 5 10 17
2 1 22 4 2
>>> diff = df.max() - df.min()
>>> diff
Col1 6
Col2 21
Col3 7
Col2500 15
>>> pd.DataFrame(diff).sort_values(by=0, ascending=False)
0
Col2 21
Col2500 15
Col3 7
Col1 6
这是我的解决办法
>>> data = {'Col1':[4,7,1],'Col2':[1,5,22], 'Col3':[3,10,4], 'Col2500':[6,17,2]}
>>> df = pd.DataFrame(data)
>>> df
Col1 Col2 Col3 Col2500
0 4 1 3 6
1 7 5 10 17
2 1 22 4 2
>>> diff = df.max() - df.min()
>>> diff
Col1 6
Col2 21
Col3 7
Col2500 15
>>> pd.DataFrame(diff).sort_values(by=0, ascending=False)
0
Col2 21
Col2500 15
Col3 7
Col1 6
希望这有帮助
diff = df.max() - df.min()
diff.sort_values()
例如:
df
Col1 Col2 Col3 Col2500
0 4 1 3 6
1 7 5 10 17
2 1 22 4 2
df.T.apply(lambda x: x.max() - x.min(), axis=1).nlargest(3)
Col2 21
Col2500 15
Col3 7
dtype: int64
>>> df.values
array([[ 0, 12, 42],
[ 1, 13, 21],
[ 12, 1, 30],
[ 3, 45, -39],
[ 4, 1, 38]])
>>> diff = df.max() - df.min()
>>>
>>> diff.sort_values(ascending=False)
T3 81
T2 44
T1 12
dtype: int64
>>> diff.sort_values()
T1 12
T2 44
T3 81
dtype: int64
>>>
希望这有帮助
diff = df.max() - df.min()
diff.sort_values()
例如:
df
Col1 Col2 Col3 Col2500
0 4 1 3 6
1 7 5 10 17
2 1 22 4 2
df.T.apply(lambda x: x.max() - x.min(), axis=1).nlargest(3)
Col2 21
Col2500 15
Col3 7
dtype: int64
>>> df.values
array([[ 0, 12, 42],
[ 1, 13, 21],
[ 12, 1, 30],
[ 3, 45, -39],
[ 4, 1, 38]])
>>> diff = df.max() - df.min()
>>>
>>> diff.sort_values(ascending=False)
T3 81
T2 44
T1 12
dtype: int64
>>> diff.sort_values()
T1 12
T2 44
T3 81
dtype: int64
>>>
您可以添加一些示例数据ato问题吗?diff=[max(col)-min(col)用于列中的列]
和sorted(diff)[:10]
?您可以添加一些示例数据ato问题吗?diff=[max(col)-min(col)用于列中的列]
和sorted(diff)[:10]
?