Pandas: if row in column A contains "x", write "y" to row in column B

Question

For pandas , I'm looking for a way to write conditional values to each row in column B, based on substrings for corresponding rows in column A.

So if cell in A contains "BULL" , write "Long" to B . Or if cell in A contains "BEAR" , write "Short" to B .

Desired output:

A                  B
"BULL APPLE X5"    "Long"
"BEAR APPLE X5"    "Short"
"BULL APPLE X5"    "Long"

B is initially empty: df = pd.DataFrame([['BULL APPLE X5',''],['BEAR APPLE X5',''],['BULL APPLE X5','']],columns=['A','B'])

Answer 1

Your code would error as you creating the Dataframe incorrectly, just create a single column A then add B based on A :

import pandas as pd
df = pd.DataFrame(["BULL","BEAR","BULL"], columns=['A'])
df["B"] = ["Long" if ele  == "BULL" else "Short" for ele in df["A"]]

print(df)

    A      B
0  BULL   Long
1  BEAR  Short
2  BULL   Long

Or do you logic with the data before you create the dataframe:

import pandas as pd
data = ["BULL","BEAR","BULL"]
data2 = ["Long" if ele  == "BULL" else "Short" for ele in data]
df = pd.DataFrame(list(zip(data, data2)), columns=['A','B'])

print(df)
      A      B
 0  BULL   Long
 1  BEAR  Short
 2  BULL   Long

For your edit:

df = pd.DataFrame([['BULL APPLE X5',''],['BEAR APPLE X5',''],['BULL APPLE X5','']], columns=['A','B'])

df["B"] = df["A"].map(lambda x: "Long" if "BULL" in x else "Short" if "BEAR" in x else "")

print(df)

            A      B
0  BULL APPLE X5   Long
1  BEAR APPLE X5  Short
2  BULL APPLE X5   Long

Or just add the column after:

df = pd.DataFrame(['BULL APPLE X5','BEAR APPLE X5','BLL APPLE X5'], columns=['A'])

df["B"] = df["A"].map(lambda x: "Long" if "BULL" in x else "Short" if "BEAR" in x else "")

print(df)

Or using contains:

df = pd.DataFrame([['BULL APPLE X5',''],['BEAR APPLE X5',''],['BULL APPLE X5','']], columns=['A','B'])


df["B"][df['A'].str.contains("BULL")] = "Long"
df["B"][df['A'].str.contains("BEAR")] = "Short"

print(df)
0  BULL APPLE X5   Long
1  BEAR APPLE X5  Short
2  BULL APPLE X5   Long

Answer 2

Also, for populating the df['B'] you can try the below method -

def applyFunc(s):
    if s == 'BULL':
        return 'Long'
    elif s == 'BEAR':
        return 'Short'
    return ''

df['B'] = df['A'].apply(applyFunc)
df
>>
       A      B
0  BULL   Long
1  BEAR  Short
2  BULL   Long

What the apply function does, is that for each row value of df['A'] , it calls the applyFunc function with the parameter as the value of that row , and the returned value is put into the same row for df['B'] , what really happens behind the scene is a bit different though, the value is not directly put into df['B'] but rather a new Series is created and at the end, the new Series is assigned to df['B'] .

Answer 3

You could use str.extract to search for regex pattern BULL|BEAR , and then use Series.map to replace those strings with Long or Short :

In [50]: df = pd.DataFrame([['BULL APPLE X5',''],['BEAR APPLE X5',''],['BULL APPLE X5','']],columns=['A','B'])

In [51]: df['B'] = df['A'].str.extract(r'(BULL|BEAR)').map({'BULL':'Long', 'BEAR':'Short'})

In [55]: df
Out[55]: 
               A      B
0  BULL APPLE X5   Long
1  BEAR APPLE X5  Short
2  BULL APPLE X5   Long

However, forming the intermediate Series with str.extract is quite slow compared to df['A'].map(lambda x:...) . Using IPython's %timeit to time the benchmarks,

In [5]: df = pd.concat([df]*10000)

In [6]: %timeit df['A'].str.extract(r'(BULL|BEAR)').map({'BULL':'Long', 'BEAR':'Short'})
10 loops, best of 3: 39.7 ms per loop

In [7]: %timeit df["A"].map(lambda x: "Long" if "BULL" in x else "Short" if "BEAR" in x else "")
100 loops, best of 3: 4.98 ms per loop

The majority of time is spent in str.extract :

In [8]: %timeit df['A'].str.extract(r'(BULL|BEAR)')
10 loops, best of 3: 37.1 ms per loop

while the call to Series.map is relatively fast:

In [9]: x = df['A'].str.extract(r'(BULL|BEAR)')

In [10]: %timeit x.map({'BULL':'Long', 'BEAR':'Short'})
1000 loops, best of 3: 1.82 ms per loop

Answer 4

Alternatively you can use np.select if you don't mind using NumPy :

import numpy as np

df['B'] = np.select(
    [df['A'].str.contains('BULL'), df['A'].str.contains('BEAR')],
    ['Long', 'Short'],
    default=np.nan,
)

If neither BULL or BEAR is found, NaN is returned.