How to conditionally replace NaN values in a dataframe?

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This is a sample of my dataset

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?

pandas dictionary python
2 Answers

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"]))
user_158730430

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"])
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