How to append two DataFrames in Pandas?

To append the rows of one DataFrame with the rows of another, we can use the Pandas append() function. With the help of append(), we can combine DataFrames vertically. Let's see how to use this method with examples.

Note: The append() method is deprecated since Pandas 1.4.0. Use pd.concat() instead for new code.

Steps to Append DataFrames

• Create two DataFrames with data
• Use append() method or pd.concat() to combine them
• Set ignore_index=True to reset row indices
• Handle different column names appropriately

Example 1: Appending DataFrames with Same Columns

When DataFrames have the same column names, appending works smoothly ?

import pandas as pd

df1 = pd.DataFrame({"x": [5, 2], "y": [4, 7], "z": [9, 3]})
df2 = pd.DataFrame({"x": [1, 3], "y": [1, 9], "z": [29, 30]})

print("Input DataFrame 1 is:")
print(df1)
print("\nInput DataFrame 2 is:")
print(df2)

df3 = df1.append(df2, ignore_index=True)
print("\nAfter appending, DataFrame is:")
print(df3)

Input DataFrame 1 is:
   x  y  z
0  5  4  9
1  2  7  3

Input DataFrame 2 is:
   x  y   z
0  1  1  29
1  3  9  30

After appending, DataFrame is:
   x  y   z
0  5  4   9
1  2  7   3
2  1  1  29
3  3  9  30

Example 2: Appending DataFrames with Different Columns

When DataFrames have different column names, missing values are filled with NaN ?

import pandas as pd

df1 = pd.DataFrame({"x": [5, 2], "y": [4, 7], "z": [9, 3]})
df2 = pd.DataFrame({"a": [1, 3], "b": [1, 9], "c": [29, 30]})

print("Input DataFrame 1 is:")
print(df1)
print("\nInput DataFrame 2 is:")
print(df2)

df3 = df1.append(df2)
print("\nAfter appending, DataFrame is:")
print(df3)

Input DataFrame 1 is:
   x  y  z
0  5  4  9
1  2  7  3

Input DataFrame 2 is:
   a  b   c
0  1  1  29
1  3  9  30

After appending, DataFrame is:
     x    y    z    a    b     c
0  5.0  4.0  9.0  NaN  NaN   NaN
1  2.0  7.0  3.0  NaN  NaN   NaN
0  NaN  NaN  NaN  1.0  1.0  29.0
1  NaN  NaN  NaN  3.0  9.0  30.0

Modern Approach: Using pd.concat()

The recommended way to append DataFrames is using pd.concat() ?

import pandas as pd

df1 = pd.DataFrame({"x": [5, 2], "y": [4, 7], "z": [9, 3]})
df2 = pd.DataFrame({"x": [1, 3], "y": [1, 9], "z": [29, 30]})

# Using pd.concat() instead of append()
df3 = pd.concat([df1, df2], ignore_index=True)
print("Using pd.concat():")
print(df3)

Using pd.concat():
   x  y   z
0  5  4   9
1  2  7   3
2  1  1  29
3  3  9  30

Key Parameters

• ignore_index=True - Resets row indices to 0, 1, 2...
• sort=False - Prevents automatic column sorting
• verify_integrity=True - Checks for duplicate indices

Conclusion

Use pd.concat() for appending DataFrames as append() is deprecated. Set ignore_index=True to reset indices. DataFrames with different columns will have NaN values in missing positions.