I am looking for a better way to replace all column values with some other value.
What I currently have is this:
gender_text = ['undefined', 'male', 'female']
df.loc[df['gender'] == 0, 'gender'] = gender_text[0]
df.loc[df['gender'] == 1, 'gender'] = gender_text[1]
df.loc[df['gender'] == 2, 'gender'] = gender_text[2]
df.head()
I was hoping for something a bit more elegant and use the gender value (0, 1 or 2) as the index to choose from in gender_text to have everything fit in one line.
You can use a dictionary.
import pandas as pd
df = pd.DataFrame({'gender':[0,0,2,1,1,2]})
gender_text = {0:'undefined', 1:'male', 2:'female'}
df['gender'].map(gender_text)
# Out[33]:
# 0 undefined
# 1 undefined
# 2 female
# 3 male
# 4 male
# 5 female
# Name: gender, dtype: object
Alternatively, you can also pd.merge, which might be better for larger datasets.
import pandas as pd
df = pd.DataFrame({'gender':[0,0,2,1,1,2]})
df_map = pd.DataFrame({'gender': [0, 1, 2], 'gender_new': ['undefined', 'male', 'female']})
df['gender'] = df.merge(df_map, on=['gender'])['gender_new']
You can define a dict
replace_values = {0 :'undefined', 1 : 'male', 2 : 'female'}
And replace multiple values using replace
df = df.replace({"gender": replace_values})
Alternatively, replace each value in the column using
df.gender = df.gender.replace(0, 'undefined')
df.gender = df.gender.replace(1, 'male')
df.gender = df.gender.replace(2, 'female')
This is one of the usecase of the map function (use np.select for much faster performance)-
gender_text = {0 :'undefined', 1 : 'male', 2 : 'female'}
df['gender'] = df['gender'].map(gender_text)
Or you can use apply -
df['gender'] = df['gender'].apply(lambda x : gender_text[x])
Or you can use np.select
condlist = [df['gender'] == 0,
df['gender'] == 1,
df['gender'] == 2]
choicelist = ['undefined',
'male',
'female']
df['gender'] = np.select(condlist, choicelist)
Performace Comparison. — >
%timeit df['gender'] = df['gender'].map(gender_text)
411 µs ± 10.9 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
%timeit df['gender'] = np.select(condlist,choicelist)
101 µs ± 322 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)
