Change all the values of a data frame based on conditions in pandas

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I am trying to change the values of a data frame based on different conditions in python.

Here is an example of my dataset

df1=pd.DataFrame(np.array([['A',1, 2, 'NaN'], ['B',-4, 5, -6], ['C',7, 0, 9]]), columns=['Details','a', 'b', 'c'])

and i want to change the values as follwoing,

df1=pd.DataFrame(np.array([['A','big', 'big', 'No data'], ['B','small', 'big', 'small'], ['C','big', 'zero', 'big']]),columns=['Details','a', 'b', 'c'])

I have tried using the following line but this did not work. I am wondering if there is any easy way to do this. thanks in advance

df1[df1.columns[1:]] = df1.iloc[:,1:].where(df1.iloc[:,1:] >0 ,'big')

pandas dataframe numpy python
1 Answers

Use numpy.select only for selected columns:

c = ['a','b','c']

#if necessary convert to numeric
df1[c] = df1[c].astype(float)

df1[c] = np.select([df1[c] > 0, df1[c] <= 0], ['big','small'], default='No data') 
print (df1)
  Details      a      b        c
0       A    big    big  No data
1       B  small    big    small
2       C    big  small      big

If need test all columns without first:

df1.iloc[:, 1:] = np.select([df1.iloc[:, 1:] > 0, 
                             df1.iloc[:, 1:] <= 0], ['big','small'], default='No data')
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