Add the missing value from one dataframe column to another column using python p

When working with dataframes in Python, it is common to encounter situations where we need to add missing values from one column to another column. In this article, we will explore three different ways to solve this problem using Python.

Method 1: Using the fillna() function

The first method involves using the fillna() function provided by the pandas library. This function allows us to fill missing values in a column with a specified value. To add the missing values from one column to another, we can simply use the fillna() function on the target column, passing the source column as the argument.


import pandas as pd

# Create two dataframes with missing values
df1 = pd.DataFrame({'A': [1, 2, None, 4, 5]})
df2 = pd.DataFrame({'B': [None, 2, 3, None, 5]})

# Add missing values from column A to column B
df2['B'] = df2['B'].fillna(df1['A'])

print(df2)

This method is simple and concise, as it leverages the built-in functionality of the pandas library. However, it modifies the original dataframe, which may not be desirable in some cases.

Method 2: Using the combine_first() function

The second method involves using the combine_first() function provided by the pandas library. This function allows us to combine two dataframes, filling missing values in one dataframe with values from another dataframe. To add the missing values from one column to another, we can create a new dataframe by combining the two columns using the combine_first() function.


import pandas as pd

# Create two dataframes with missing values
df1 = pd.DataFrame({'A': [1, 2, None, 4, 5]})
df2 = pd.DataFrame({'B': [None, 2, 3, None, 5]})

# Add missing values from column A to column B
df3 = df2.combine_first(df1)

print(df3)

This method creates a new dataframe, leaving the original dataframes unchanged. It provides more flexibility in terms of handling missing values, as it allows us to combine multiple columns or even multiple dataframes.

Method 3: Using the fillna() method with a dictionary

The third method involves using the fillna() method with a dictionary. This method allows us to specify different values for different columns when filling missing values. To add the missing values from one column to another, we can create a dictionary mapping the target column to the source column and pass it to the fillna() method.


import pandas as pd

# Create two dataframes with missing values
df1 = pd.DataFrame({'A': [1, 2, None, 4, 5]})
df2 = pd.DataFrame({'B': [None, 2, 3, None, 5]})

# Add missing values from column A to column B
df2['B'] = df2['B'].fillna({'B': df1['A']})

print(df2)

This method provides fine-grained control over which columns to fill and with what values. It is particularly useful when dealing with dataframes containing multiple columns with missing values.

After exploring these three methods, it is clear that the best option depends on the specific requirements of the problem at hand. If simplicity and modifying the original dataframe are preferred, Method 1 using the fillna() function is a good choice. If creating a new dataframe and more flexibility are desired, Method 2 using the combine_first() function is recommended. Finally, if fine-grained control over filling missing values is necessary, Method 3 using the fillna() method with a dictionary is the way to go.

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5 Responses

  1. Method 1 seems more straightforward, but Method 3 with a dictionary looks interesting. What do you guys think?

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