[英]In Pandas, how do I apply a function to a row of a dataframe, where each item in the row should be passed to the function as an argument?
[英]How do I apply a function to a pandas dataframe?
我試圖將函數應用於這樣的熊貓數據框
fogo = intervalo.resample('D', how = ['max']).TMP
fogo['Tmin'] = intervalo.resample('D', how = ['min']).TMP
fogo['Rain'] = intervalo.resample('D', how = ['sum']).RNF
fogo.columns = ['TMax','TMin','Rain']
fogo['Fogo'] = (fogo['TMax']>24) \
| ((fogo['TMax']>21) & (fogo['TMin']>12)) \
| ((fogo['TMax']>18) & (fogo['TMin']>10) & (fogo['Rain']>2))
def f(x):
if (fogo['TMax']>24):
return 'a'
elif ((fogo['TMax']>21) & (fogo['TMin']>12)):
return 'b'
elif ((fogo['TMax']>18) & (fogo['TMin']>10) & (fogo['Rain']>2)):
return 'c'
fogo['Causa'] = fogo.apply(f, axis=1)
TMax TMin Rain Fogo Causa
2012-04-01 21.6 10.3 0.8 False empty
2012-04-02 19.3 9.5 0.0 False empty
2012-04-03 16.2 10.1 0.2 False empty
2012-04-04 16.7 11.4 0.2 False empty
2012-04-05 14.0 5.9 2.9 False empty
但它返回以下錯誤
'The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().
你可以幫幫我嗎?
謝謝
雨果
因此,代碼中的第一個問題是您正在調用Apply並設置param axis=1
這將按行應用函數,這很好。
但是,在函數中,當您調用fogo ['TMax']> 24時,您正在引用整個數據幀,這就是為什么在逐行應用函數但嘗試引用整個數據幀時會收到錯誤的原因。
因此,您可以將函數更改為此:
def f(x):
if (x['TMax']>24):
return 'a'
elif ((x['TMax']>21) & (x['TMin']>12)):
return 'b'
elif ((x['TMax']>18) & (x['TMin']>10) & (x['Rain']>2)):
return 'c'
但是,看到您只是為3個不同的條件設置了三個值,那么您就可以使用布爾索引創建掩碼,並設置所有符合條件的行。
所以:
fogo.loc[fogo['TMax']> 24,'Causa'] = 'a'
fogo.loc[(fogo['TMax']> 21) & (fogo['TMin'] > 12),'Causa'] = 'b'
fogo.loc[(fogo['TMax']> 18) & (fogo['TMin'] > 10) & (fogo['Rain'] > 2),'Causa'] = 'c'
這將比逐行迭代要快得多,尤其是對於大型數據幀而言。
因此,在您的示例數據上,我可以執行以下操作:
In [10]:
fogo.loc[fogo['TMax']> 21,'Causa'] = 'a'
fogo.loc[(fogo['TMax']> 21) & (fogo['TMin'] > 11),'Causa'] = 'b'
fogo.loc[(fogo['TMax']> 11) & (fogo['TMin'] > 5) & (fogo['Rain'] > 2),'Causa'] = 'c'
fogo
Out[10]:
TMax TMin Rain Fogo Causa
2012-04-01 21.6 10.3 0.8 False a
2012-04-02 19.3 9.5 0.0 False empty
2012-04-03 16.2 10.1 0.2 False b
2012-04-04 16.7 11.4 0.2 False b
2012-04-05 14.0 5.9 2.9 False c
[5 rows x 5 columns]
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