Replacing values of dataframe with null values and yes and no with 1 and 0-CodePudding

I've got a pandas DataFrame that looks like this:

  molecule            species
0        a              dog
1        b            horse
2        c              []
3        d             pig
4        e              []

I want to replace the [] value with NaN using python. How can I achieve this?

For testing:

df = pd.DataFrame({
    'molecule': ['a','b','c','d','e'],
    'species' : ['horse','cat','[]','frog','lion']})

CodePudding user response：

You can try this

import numpy as np
df = pd.DataFrame({'molecule': ['a','b','c','d','e'], 'species' : ['horse','cat','[]','frog','lion']})
df["species"].replace({"[]":np.nan})

replace will take dictionary where you can tell what to be mapped to what.

CodePudding user response：

You can also replace values with NAN with DataFrame.mask() methods. The mask() method replaces the values of the rows where the condition evaluates to True.

import numpy as np
import pandas as pd
df = pd.DataFrame({'molecule': ['a','b','c','d','e'], 'species' : ['horse','cat','[]','frog','lion']})
df=df.mask(df == '[]')

CodePudding user response：

df.loc[df.species.eq('[]'), 'species'] = np.nan