I want to replace the values of specific columns. I can change the values one by one but, I have hundreds of columns and I need to change the columns starting with a specific string. Here is an example, I want to replace the string when the column name starts with "Q14"
df.filter(regex = 'Q14').replace(1, 'Selected').replace(0, 'Not selected')
The above code is working. But, how I can implement it in my dataframe? As this is the function so I can't use inplace.
Consider below df:
In [439]: df = pd.DataFrame({'Q14_A':[ 1,0,0,2], 'Q14_B':[0,1,1,2], 'Q12_A':[1,0,0,0]})
In [440]: df
Out[440]:
Q14_A Q14_B Q12_A
0 1 0 1
1 0 1 0
2 0 1 0
3 2 2 0
Filter columns that start with Q14, save it in a variable:
In [443]: cols = df.filter(regex='^Q14').columns
Now, change the above selected columns with your replace commands:
In [446]: df[cols] = df[cols].replace(1, 'Selected').replace(0, 'Not selected')
Output:
In [447]: df
Out[447]:
Q14_A Q14_B Q12_A
0 Selected Not selected 1
1 Not selected Selected 0
2 Not selected Selected 0
3 2 2 0
You can iterate over all columns and based on matched condition apply column transformation using apply command:
for column in df.columns:
if column.startswith("Q"):
df[column] = df[column].apply(lambda x: "Selected" if x == 1 else "Not selected")
Using pandas.Series.replace dict
df = pd.DataFrame({'Q14_A':[ 1,0,0,2], 'Q14_B':[0,1,1,2], 'Q12_A':[1,0,0,0]})
cols = df.filter(regex='^Q14').columns
replace_map = {
1: "Selected",
0 : "Not Selected"
}
df[cols] = df[cols].replace(replace_map)
