How to separate Numbers from string and move them to next column in Python?

Question

I am working on a share market data and in some columns market cap has shifted to previous column. I am trying to fetch them in next column but the value it’s returning is completely different.

This is the code I am using –

data['Market Cap (Crores)']=data['Sub-Sector'].astype('str').str.extractall('(d+)').unstack().fillna('').sum(axis=1).astype(int)
data['Market Cap (Crores)']

But the output I am getting is

968             NaN
969             NaN
970   -2.147484e+09
971   -2.147484e+09
972   -2.147484e+09

How do I get the correct values?

Asked By: jatin

||

Source

Answer 1

You just do it, step by step. First, pick out the rows that need fixing (where the market cap is Nan). Then, I create two functions, one to pull the market cap from the string, one to remove the market cap. I use apply to fix up the rows, and substitute the values into the original dataframe.

import pandas as pd
import numpy as np

data = [
    ['GNA Axles Ltd', 'Auto Parts', 1138.846797],
    ['Andhra Paper Ltd', 'Paper Products', 1135.434614],
    ['Tarc', 'Real Estate 1134.645409',  np.NaN],
    ['Udaipur Cement Works', 'Cement 1133.531734', np.NaN],
    ['Pnb Gifts', 'Investment Banking 1130.463641', np.NaN],
]

def getprice(row):
    return float(row['Sub-Sector'].split()[-1])

def removeprice(row):
    return ' '.join(row['Sub-Sector'].split()[:-1])

df = pd.DataFrame( data, columns= ['Company','Sub-Sector','Market Cap (Crores)'] )
print(df)

picks = df['Market Cap (Crores)'].isna()
rows = df[picks]
print(rows)

df.loc[picks,'Sub-Sector'] = rows.apply(removeprice, axis=1)
df.loc[picks,'Market Cap (Crores)'] = rows.apply(getprice, axis=1)
print(df)

Output:

                Company                      Sub-Sector  Market Cap (Crores)
0         GNA Axles Ltd                      Auto Parts          1138.846797
1      Andhra Paper Ltd                  Paper Products          1135.434614
2                  Tarc         Real Estate 1134.645409                  NaN
3  Udaipur Cement Works              Cement 1133.531734                  NaN
4             Pnb Gifts  Investment Banking 1130.463641                  NaN
                Company                      Sub-Sector  Market Cap (Crores)
2                  Tarc         Real Estate 1134.645409                  NaN
3  Udaipur Cement Works              Cement 1133.531734                  NaN
4             Pnb Gifts  Investment Banking 1130.463641                  NaN
                Company          Sub-Sector  Market Cap (Crores)
0         GNA Axles Ltd          Auto Parts          1138.846797
1      Andhra Paper Ltd      Paper Products          1135.434614
2                  Tarc         Real Estate          1134.645409
3  Udaipur Cement Works              Cement          1133.531734
4             Pnb Gifts  Investment Banking          1130.463641

Answered By: Tim Roberts

Answer 2

df['Sub-Sector Number'] = df['Sub-Sector'].astype('str').str.extractall('(d+)').unstack().fillna('').sum(axis=1).astype(int)
df['Sub-Sector final'] = df[['Sub-Sector Number','Sub-Sector']].ffill(axis=1).iloc[:,-1]
df

Hi there,

Here is the method which you can try, use your code to create a numeric field and select non-missing value from Sub-Sector Number and Sub-Sector creating your final field – Sub-Sector final

Please try it and if not working please let me know

Thanks Leon

Answered By: yuehengli

How to separate Numbers from string and move them to next column in Python?

Question:

Answers: