Python Dataframe: change values of a specified column of a Dataframe

Question

I have the following Dataframe object df:

    A        B         C       
0  0.0      5.0       0.0        
1  0.0      6.0       0.0        
2  0.0      9.0       0.0        
3  0.0      0.0       0.0        
4  0.0      2.0       0.0        
5  0.0      5.0       0.0        
6  6.0      0.0       0.0        
7  0.0      0.0       0.0        
8  0.0      1.0       0.0

I want to change the values of column 'B':

If the value is smaller than 3, than the value should be replaced with 'False'.

Otherwise the value should be replaced with 'True'.

I tried:

df['B'] = df['B'].apply(lambda x: [False if y < 3 else True for y in x])

In this case I get the TypeError: 'float' object is not iterable.

When I use it for the whole Dataframe it works though:

df = df.apply(lambda x: [False if y < 3 else True for y in x])

Any help will be appreciated.

Space Impact · Accepted Answer · 2018-09-28 09:33:41Z

2

Use direct comparison or ge as:

df['B'] = df['B']>=3

OR

df['B'] = df['B'].ge(3)

print(df)

     A      B    C
0  0.0   True  0.0
1  0.0   True  0.0
2  0.0   True  0.0
3  0.0  False  0.0
4  0.0  False  0.0
5  0.0   True  0.0
6  6.0  False  0.0
7  0.0  False  0.0
8  0.0  False  0.0

answered Sep 28, 2018 at 9:33

Space Impact

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Comments

Karn Kumar · Accepted Answer · 2018-09-28 09:42:39Z

1

simply as:

>>> import pandas as pd
>>> df = pd.DataFrame([1,2,3,4], columns=["data"])
>>> df["B"] = df["data"] > 2
>>> df
   data      B
0     1  False
1     2  False
2     3   True
3     4   True
>>>

You can even include numpy for conditional comparison like below:

>>> import pandas as pd
>>> import numpy as np
>>> df = pd.DataFrame([1,2,3,4], columns=["A"])
>>> df["B"] = np.where(df["A"] <2, "False", "True")
>>> df
   A      B
0  1  False
1  2   True
2  3   True
3  4   True

answered Sep 28, 2018 at 9:42

Karn Kumar

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Comments

JON · Accepted Answer · 2018-09-28 10:15:44Z

1

Below code may help your approach, when apply method get used in column then it gets all the values of that column so i don't think to use for loop here

df['b'] = df['b'].apply(lambda x: True if x>3 else False)

answered Sep 28, 2018 at 10:15

JON

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Comments

Charles R · Accepted Answer · 2018-09-28 09:35:34Z

0

df.loc[df.B > 3, 'new'] = True
df.loc[df.B < 3,'new'] = False
df = df.drop("B", axis=1)
df = df.rename(columns={"new": "B"})

answered Sep 28, 2018 at 9:35

Charles R

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