python - How do I apply a function to a pandas dataframe? -

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i have tried apply function pandas dataframe this

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

but returns following error

'the truth value of series ambiguous. use a.empty, a.bool(), a.item(), a.any() or a.all().

could me?

thank you

hugo

so first problem in code calling apply , setting param axis=1 applies function row-wise fine.

however, in function referencing whole dataframe when call fogo['tmax'] > 24, why error applying function row-wise trying reference entire dataframe confused.

so change function this:

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'

however, seeing setting 3 values 3 different conditions use boolean indexing create mask , set rows meet conditions.

so:

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'

this faster iterating row-wise large dataframes.

so on example data can this:

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      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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