Add Rows from Pandas DataFrame to Another

Welcome to this tutorial on how to add a row from one Pandas DataFrame to another.

In this tutorial, you’ll learn the different methods for adding rows from one DataFrame to another.

 

 

Using concat()

The concat() function in Pandas is a powerful tool for combining DataFrames. First, let’s create two sample DataFrames.

import pandas as pd
df1 = pd.DataFrame({
    'Plan_ID': [1, 2, 3],
    'Monthly_Cost': [30, 40, 50],
    'Data_Limit': [2, 4, 6]
})
df2 = pd.DataFrame({
    'Plan_ID': [4],
    'Monthly_Cost': [60],
    'Data_Limit': [8]
})
print("DataFrame 1:")
print(df1)
print("\nDataFrame 2:")
print(df2)

Output:

DataFrame 1:
   Plan_ID  Monthly_Cost  Data_Limit
0        1            30           2
1        2            40           4
2        3            50           6

DataFrame 2:
   Plan_ID  Monthly_Cost  Data_Limit
0        4            60           8

Now, let’s use the concat() function to combine these DataFrames.

result_df = pd.concat([df1, df2], ignore_index=True)
print("Resulting DataFrame:")
print(result_df)

Output:

Resulting DataFrame:
   Plan_ID  Monthly_Cost  Data_Limit
0        1            30           2
1        2            40           4
2        3            50           6
3        4            60           8

In the output, you can see that the row from df2 has been successfully added to df1.

We used ignore_index=True to reindex the resulting DataFrame.

 

Using loc and iloc

These properties allow for label-based and integer-based indexing, respectively, and can be useful for more granular control over row addition.
We continue with the same df1 and df2 DataFrames as in the previous examples.

Using loc

The loc attribute allows you to add a row by specifying the label of the new index.

# Add row using loc
df1.loc[len(df1)] = df2.loc[0]
print("Resulting DataFrame:")
print(df1)

Output:

Resulting DataFrame:
   Plan_ID  Monthly_Cost  Data_Limit
0        1            30           2
1        2            40           4
2        3            50           6
3        4            60           8

Here, we added the row from df2 to df1 using the loc attribute. We specified the new index as len(df1), which appends the row at the end.

Using iloc

Similarly, you can use iloc for integer-location based indexing to add rows.

# Reset df1 for demonstration
df1 = df1.iloc[:-1]

# Add row using iloc
df1.loc[len(df1)] = df2.iloc[0]
print("Resulting DataFrame:")
print(df1)

Output:

Resulting DataFrame:
   Plan_ID  Monthly_Cost  Data_Limit
0        1            30           2
1        2            40           4
2        3            50           6
3        4            60           8

 

Conditional Addition of Rows Based on Column Values

This conditional addition can be achieved using boolean indexing.

First, let’s create two sample DataFrames:

df6 = pd.DataFrame({
    'Plan_ID': [1, 2, 3],
    'Monthly_Cost': [30, 40, 50],
    'Data_Limit': [2, 4, 6]
})
df7 = pd.DataFrame({
    'Plan_ID': [2, 3, 4],
    'Monthly_Cost': [35, 55, 60],
    'Data_Limit': [4, 7, 8]
})
print("DataFrame 6:")
print(df6)
print("\nDataFrame 7:")
print(df7)

Output:

DataFrame 6:
   Plan_ID  Monthly_Cost  Data_Limit
0        1            30           2
1        2            40           4
2        3            50           6

DataFrame 7:
   Plan_ID  Monthly_Cost  Data_Limit
0        2            35           4
1        3            55           7
2        4            60           8

Suppose you want to add only the rows from DataFrame df7 where the Monthly_Cost is greater than 50 to df6.

filtered_df7 = df7[df7['Monthly_Cost'] > 50]
result_df_conditional = df6.append(filtered_df7, ignore_index=True, sort=False)
print("Resulting DataFrame:")
print(result_df_conditional)

Output:

Resulting DataFrame:
   Plan_ID  Monthly_Cost  Data_Limit
0        1            30           2
1        2            40           4
2        3            50           6
3        3            55           7
4        4            60           8

 

Adding Rows from Multiple DataFrames (Batch Addition)

First, let’s create three DataFrames to simulate a situation where you need to combine rows from multiple sources.

df8 = pd.DataFrame({
    'Plan_ID': [1, 2],
    'Monthly_Cost': [20, 25],
    'Data_Limit': [1, 2]
})
df9 = pd.DataFrame({
    'Plan_ID': [3, 4],
    'Monthly_Cost': [30, 35],
    'Data_Limit': [3, 4]
})
df10 = pd.DataFrame({
    'Plan_ID': [5, 6],
    'Monthly_Cost': [40, 45],
    'Data_Limit': [5, 6]
})
print("DataFrame 8:")
print(df8)
print("\nDataFrame 9:")
print(df9)
print("\nDataFrame 10:")
print(df10)

Output:

DataFrame 8:
   Plan_ID  Monthly_Cost  Data_Limit
0        1            20           1
1        2            25           2

DataFrame 9:
   Plan_ID  Monthly_Cost  Data_Limit
0        3            30           3
1        4            35           4

DataFrame 10:
   Plan_ID  Monthly_Cost  Data_Limit
0        5            40           5
1        6            45           6

One of the simplest ways to perform batch row addition is by using the concat() function, where you can pass a list of DataFrames to concatenate.

result_df_batch = pd.concat([df8, df9, df10], ignore_index=True, sort=False)
print("Resulting DataFrame:")
print(result_df_batch)

Output:

Resulting DataFrame:
   Plan_ID  Monthly_Cost  Data_Limit
0        1            20           1
1        2            25           2
2        3            30           3
3        4            35           4
4        5            40           5
5        6            45           6