Replace row in DataFrame dependant on a value

Question

I would like to replace an entire row in a data, subject to one column in that row satisfying the condition. i.e. where red is the dataframe

['1' 0.0 ' P']
['2' 0.0 ' S']
['3' 64  ' M']
['4' 70  ' M']



red=red.replace(to_replace=' M', value=0)

returns the result:

['1' 0.0 ' P']
['2' 0.0 ' S']
['3' 64  0]
['4' 70  0]

but I would like it to return:

['1' 0.0 ' P']
['2' 0.0 ' S']
['3' 0  0]
['4' 0  0]

Zeugma · Accepted Answer · 2013-10-17 12:38:45Z

6

Use loc to filter out the part of the DataFrame you want to zero, and then assign the value to it. Below, it selects all lines where c column value is 'M' and it takes all columns from b to c, and set the value of this selection to 0:

df = pd.DataFrame([['1', 0.0, 'P'],
    ...: ['2', 0.0, 'S'],
    ...: ['3', 64,  'M'],
    ...: ['4', 70,  'M'],], columns=['a', 'b', 'c'])

df.loc[df['c']=='M','b':'c'] = 0

df
Out[54]: 
   a  b  c
0  1  0  P
1  2  0  S
2  3  0  0
3  4  0  0

answered Oct 17, 2013 at 12:38

Zeugma

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2 Comments

tuxErrante Over a year ago

Thanks for your answer, but how to select values in a range ? df.loc[ df['c']>0 and df['c']<1,'b':'c'] = 0 will raise an error

Zeugma Over a year ago

You forget the parenthesis, please see link here

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