How to conditionally replace NaN values in a dataframe?-CodePudding

I want to replace nan values in column WL1 with values in a dictionary according to the Month column

This is the dictionary: {'WL1': {1: 176.316, 2: 176.296, 3: 176.2825, 4: 176.398, 5: 176.52, 6: 176.576, 7: 176.558, 8: 176.519, 9: 176.479, 10: 176.382, 11: 176.36, 12: 176.353}}

For example, if df['Month'] == 1 and the value in WL1 is a NaN value, then we replace the nan with 176.326. If there is no nan values, we replace nothing.

Could anyone please show me how to code it out?

CodePudding user response：

fillna can take a series to replace NaN values with. Non-NaN values are left untouched.

Replace the month numbers with the values from your dictionary with map, then pass the result to fillna:

df["WL1"] = df.WL1.fillna(df.Month.map(dictionary["WL1"]))

CodePudding user response：

You can convert your dictionary to pd.Series or pd.DataFrame, then merge it with the original dataset on Month column, then use fillna. Something like this:

import pandas as pd
import numpy as np

df = pd.DataFrame(dict(WL1=[np.nan, np.nan, 177.26], Month=[1, 2, 3]))
replacememnts = {
    "WL1": {
        1: 176.316,
        2: 176.296,
        3: 176.2825,
    }
}
repl_df = pd.DataFrame(dict(repl=replacememnts["WL1"]))
df.merge(repl_df, left_on="Month", right_index=True).assign(
    WL1=lambda x: x["WL1"].fillna(x["repl"])
).drop(columns=["repl"])