Delete the first three rows of a DataFrame in Pandas

To delete the first three rows of a DataFrame in Pandas, we can use the iloc[] indexer to slice the DataFrame starting from the fourth row (index 3).

Using iloc[] to Delete First Three Rows

The iloc[] method allows positional indexing. By using df.iloc[3:], we select all rows starting from index 3 onwards ?

import pandas as pd

# Create a DataFrame
df = pd.DataFrame(
    {
        "x": [5, 2, 7, 0, 7, 0, 5, 2],
        "y": [4, 7, 5, 1, 5, 1, 4, 7],
        "z": [9, 3, 5, 1, 5, 1, 9, 3]
    }
)

print("Input DataFrame:")
print(df)
print()

# Delete first 3 rows
df = df.iloc[3:]
print("After deleting the first 3 rows:")
print(df)

Input DataFrame:
   x  y  z
0  5  4  9
1  2  7  3
2  7  5  5
3  0  1  1
4  7  5  5
5  0  1  1
6  5  4  9
7  2  7  3

After deleting the first 3 rows:
   x  y  z
3  0  1  1
4  7  5  5
5  0  1  1
6  5  4  9
7  2  7  3

Alternative Method Using drop()

You can also use the drop() method to delete specific row indices ?

import pandas as pd

df = pd.DataFrame(
    {
        "x": [5, 2, 7, 0, 7, 0, 5, 2],
        "y": [4, 7, 5, 1, 5, 1, 4, 7],
        "z": [9, 3, 5, 1, 5, 1, 9, 3]
    }
)

print("Original DataFrame:")
print(df)
print()

# Delete first 3 rows using drop()
df_dropped = df.drop([0, 1, 2])
print("After dropping first 3 rows:")
print(df_dropped)

Original DataFrame:
   x  y  z
0  5  4  9
1  2  7  3
2  7  5  5
3  0  1  1
4  7  5  5
5  0  1  1
6  5  4  9
7  2  7  3

After dropping first 3 rows:
   x  y  z
3  0  1  1
4  7  5  5
5  0  1  1
6  5  4  9
7  2  7  3

Resetting the Index

After deleting rows, you might want to reset the index to start from 0 ?

import pandas as pd

df = pd.DataFrame(
    {
        "x": [5, 2, 7, 0, 7, 0, 5, 2],
        "y": [4, 7, 5, 1, 5, 1, 4, 7],
        "z": [9, 3, 5, 1, 5, 1, 9, 3]
    }
)

# Delete first 3 rows and reset index
df_reset = df.iloc[3:].reset_index(drop=True)
print("After deleting rows and resetting index:")
print(df_reset)

After deleting rows and resetting index:
   x  y  z
0  0  1  1
1  7  5  5
2  0  1  1
3  5  4  9
4  2  7  3

Conclusion

Use df.iloc[3:] for simple row slicing or df.drop([0, 1, 2]) for explicit row removal. Add reset_index(drop=True) if you need continuous indexing starting from 0.