How to delete only '0' from pandas dataframe-CodePudding

I'm reading the dataframe into pandas from a csv file.

If my data looks like this:

0                     1               2          3
 1                     0               19          12
 2                     5               0           13
 3                     6               21          0
 4                     7               4           15

How can I just remove the '0'?

I don't want to delete the entire row/ column just the value.

I want it to look like this:

0                     1               2          3
 1                     5                19           12
 2                     6                21           13
 3                     7                4            15

The other solutions I found on the internet involve dropping entire columns/ rows. I don't want to lose the other data in those columns and rows. I just don't want the 0 to interfere with my min calculation.

CodePudding user response：

df=pd.DataFrame({'1':['0','5','6','7'],'2':['19','0','21','4'],'3':['12','13','0','15']})
df=df.replace('0',np.nan)

col_1=df['1'].dropna().reset_index(drop=True)
col_2=df['2'].dropna().reset_index(drop=True)
col_3=df['3'].dropna().reset_index(drop=True)

df_new=pd.concat([col_1,col_2,col_3],axis=1)

CodePudding user response：

If your string above is df_str you can do

import io
import pandas as pd

df = df = pd.read_csv(io.StringIO(df_str), engine='python', sep='\s ', index_col=0)
df.apply(lambda x: x[x != 0].reset_index(drop=True))

and it gives you

   1   2   3
0  5  19  12
1  6  21  13
2  7   4  15