Fill df with empty rows based on index of other df-CodePudding

I am trying to use df.update(), but my dfs have different sizes. Now I want to fill up the smaler df with dummy rows to match the shape the bigger df. Here's a minimal example:

import pandas as pd
import numpy as np
data = {
    "Feat_A": ["INVALID", "INVALID", "INVALID"],
    "Feat_B": ["INVALID", "INVALID", "INVALID"],
    "Key": [12, 25, 99],
}
df = pd.DataFrame(data=data)

data = {"Feat_A": [1, np.nan], "Feat_B": [np.nan, 2], "Key": [12, 99]}
result = pd.DataFrame(data=data)

# df.update(result) not working because of different sizes/shape

# result should be
#       Feat_A  Feat_B  Key
# 0     1.0     NaN     12
# NaN   NaN     NaN     NaN
# 2     NaN     2.0     99

# df.update(result) should work now

CodePudding user response：

This did it:

df.update(result.set_index('Key').reindex(df.set_index('Key').index).reset_index())

CodePudding user response：

Does this meet your needs? Modified your example to include unique DataFrame values to confirm proper alignment:

# Modified example
data = {
    "Feat_A": ["INVALID_A12", "INVALID_A25", "INVALID_A99"],
    "Feat_B": ["INVALID_B12", "INVALID_B25", "INVALID_B99"],
    "Key": [12, 25, 99],
}
df = pd.DataFrame(data=data)

data = {"Feat_A": [1, np.nan], "Feat_B": [np.nan, 2], "Key": [12, 99]}
result = pd.DataFrame(data=data)

# Use Key column as DataFrame indexes
df = df.set_index('Key')
result = result.set_index('Key')

# Add all-NaN rows with keys that exist in df but not in result
result = result.reindex_like(df)

# Update
result.update(df)

print(result)

              Feat_A       Feat_B
    Key                          
    12   INVALID_A12  INVALID_B12
    25   INVALID_A25  INVALID_B25
    99   INVALID_A99  INVALID_B99