CODEWITHSUNDEEP
pythonpandasdataframe
CodeGuy
CodeGuy

Reputation: 28905

Convert pandas python data frame column values in place

I have a column of positive and negative numbers and I want to convert it to a list of 0s and 1s. If the number if positive, it should be replaced with a 1. If the number is negative or 0, it should be replaced by a 0. How can I do this?

For example, in R, I would do:

list = ifelse(list > 0, 1, 0)

Upvotes: 0

Views: 313

Answers (3)

beeftendon
beeftendon

Reputation: 926

You can use DataFrame.apply to apply a function to each row of your DataFrame. For instance:

import numpy as np
import pandas as pd
df = pd.DataFrame({'A': [1, -2, 3, -4]})
df['A'] = df.apply(lambda row:np.where(row['A'] > 0, 1, 0), axis=1)

The lambda function can be replaced with any function (doesn't have to be a lambda), and axis=1 is to apply the function to each row rather than each column.

http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.apply.html

Upvotes: 0

Joe T. Boka
Joe T. Boka

Reputation: 6579

You can return boolean values and use astype(int) to convert them to 1 and 0. 

print((df['A'] > 0).astype(int))

Example: 

df = pd.DataFrame({'A': [1,-1,2,-2,3,-3]})
print(df)
A
0  1
1 -1
2  2
3 -2
4  3
5 -3
print((df['A'] > 0).astype(int))
0    1
1    0
2    1
3    0
4    1
5    0

Upvotes: 1

Steven G
Steven G

Reputation: 17152

you can use an indexer and .loc to change values such has

 indexer = df[df['col']>0].index
 df.loc[indexer] = 1

  indexer_2 = df[df['col']<0].index
  df.loc[indexer_2] = 0

or you can look at numpy.where such has

import numpy as np
import pandas as pd
pd.DataFrame(np.where(df>0,1,0),index=df.index)

Upvotes: 0

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