6

I have a fairly simple question based on this sample code:

x1 = 10*np.random.randn(10,3)
df1 = pd.DataFrame(x1)

I am looking for a single DataFrame derived from df1 where positive values are replaced with "up", negative values are replaced with "down", and 0 values, if any, are replaced with "zero". I have tried using the .where() and .mask() methods but could not obtain the desired result.

I have seen other posts which filter according to multiple conditions at once, but they do not show how to replace values according to different conditions.

Georgy
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laszlopanaflex
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    Possible duplicate of [Pandas: Filtering multiple conditions](https://stackoverflow.com/questions/48978550/pandas-filtering-multiple-conditions) – FChm Mar 09 '19 at 19:40

4 Answers4

12

For multiple conditions ie. (df['employrate'] <=55) & (df['employrate'] > 50)

use this:

df['employrate'] = np.where(
   (df['employrate'] <=55) & (df['employrate'] > 50) , 11, df['employrate']
   )

or you can do it this way as well,

gm.loc[(gm['employrate'] <55) & (gm['employrate'] > 50),'employrate']=11

here informal syntax can be:

<dataset>.loc[<filter1> & (<filter2>),'<variable>']='<value>'

out[108]:
       country  employrate alcconsumption
0  Afghanistan   55.700001            .03
1      Albania   11.000000           7.29
2      Algeria   11.000000            .69
3      Andorra         nan          10.17
4       Angola   75.699997           5.57

therefore syntax we used here is:

 df['<column_name>'] = np.where((<filter 1> ) & (<filter 2>) , <new value>, df['column_name'])

for single condition, ie. ( 'employrate'] > 70 )

       country        employrate alcconsumption
0  Afghanistan  55.7000007629394            .03
1      Albania  51.4000015258789           7.29
2      Algeria              50.5            .69
3      Andorra                            10.17
4       Angola  75.6999969482422           5.57

use this: 

df.loc[df['employrate'] > 70, 'employrate'] = 7

       country  employrate alcconsumption
0  Afghanistan   55.700001            .03
1      Albania   51.400002           7.29
2      Algeria   50.500000            .69
3      Andorra         nan          10.17
4       Angola    7.000000           5.57

therefore syntax here is:

df.loc[<mask>(here mask is generating the labels to index) , <optional column(s)> ]
Harshit Jain
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8
df1.apply(np.sign).replace({-1: 'down', 1: 'up', 0: 'zero'})

Output:

      0     1     2
0  down    up    up
1    up  down  down
2    up  down  down
3  down  down    up
4  down  down    up
5  down    up    up
6  down    up  down
7    up  down  down
8    up    up  down
9  down    up    up

P.S. Getting exactly zero with randn is pretty unlikely, of course

perl
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  • sleek solution @perl... but what if i need to compute operations with the ```df1``` dataframe (let's say, x + 20 if ```np.sign(x)==-1```) and not just replacement with character strings? – Manuel F Feb 25 '21 at 11:57
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    @ManuelF That kind of replacement would be easier done with `where`: `df.where(np.sign(df)!=-1, df + 20)` (note that it replaces values where the condition is false, hence the inversion) – perl Feb 26 '21 at 09:25
7

In general, you could use np.select on the values and re-build the DataFrame

import pandas as pd
import numpy as np

df1 = pd.DataFrame(10*np.random.randn(10, 3))
df1.iloc[0, 0] = 0 # So we can check the == 0 condition 

conds = [df1.values < 0 , df1.values > 0]
choices = ['down', 'up']

pd.DataFrame(np.select(conds, choices, default='zero'),
             index=df1.index,
             columns=df1.columns)

Output:

      0     1     2
0  zero  down    up
1    up  down    up
2    up    up    up
3  down  down  down
4    up    up    up
5    up    up    up
6    up    up  down
7    up    up  down
8  down    up  down
9    up    up  down
ALollz
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4

IF condition with OR 

from pandas import DataFrame

names = {'First_name': ['Jon','Bill','Maria','Emma']}

df = DataFrame(names,columns=['First_name'])

df.loc[(df['First_name'] == 'Bill') | (df['First_name'] == 'Emma'), 'name_match'] = 'Match'  
df.loc[(df['First_name'] != 'Bill') & (df['First_name'] != 'Emma'), 'name_match'] = 'Mismatch'
print (df)

Output

  First_name name_match
0        Jon   Mismatch
1       Bill      Match
2      Maria   Mismatch
3       Emma      Match
Mohammed Shameem
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