[英]Append min value of two columns in pandas data frame
df df
Purchase
1
3
2
5
4
7
df2 df2
df2 = pd.DataFrame(columns=['Mean','Median','Max','Col4'])
df2 = df2.append({'Mean': (df['Purchase'].mean()),'Median':df['Purchase'].median(),'Max':(df['Purchase'].max()),'Col4':(df2[['Mean','Median']].min(axis=1))}, ignore_index=True)
Output obtained获得的输出
Mean Median Max Col4
3.66 3.5 7 Series([], dtype: float64)
Output expected预期产出
Mean Median Max Col4
3.66 3.5 7 3.5 #Value in Col4 is Min(Mean, Median of df2)
Can anyone help?任何人都可以帮忙吗?
Use np.minimum
and passed mean
with median
:使用
np.minimum
并通过mean
与median
:
df2 = pd.DataFrame(columns=['Mean','Median','Max','Col4'])
df2 = (df2.append({'Mean': df['Purchase'].mean(),
'Median':df['Purchase'].median(),
'Max': df['Purchase'].max(),
'Col4': np.minimum(df['Purchase'].mean(), df['Purchase'].median())},
ignore_index=True))
print (df2)
Mean Median Max Col4
0 3.666667 3.5 7.0 3.5
Or better is use Series.agg
with new value of min in next step, last create one row DataFrame:或者更好的是使用
Series.agg
在下一步分钟的新的价值,最后建立一个行数据帧:
s = df['Purchase'].agg(['mean','median','max'])
s.loc['col4'] = s[['mean','median']].min()
df = s.to_frame(0).T
print (df)
mean median max col4
0 3.666667 3.5 7.0 3.5
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