How to access a group of rows in a Pandas DataFrame?

To access a group of rows in a Pandas DataFrame, we can use the loc[] indexer. For example, if we use df.loc[2:5], then it will select all the rows from index 2 to 5 (inclusive).

Using loc[] for Row Selection

The loc[] method allows label-based indexing and supports slice notation for selecting consecutive rows ?

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

Input DataFrame is:
   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

Selecting Rows 2 to 5

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]
   }
)

selected_rows = df.loc[2:5]
print("Selected rows (2 to 5):")
print(selected_rows)

Selected rows (2 to 5):
   x  y  z
2  7  5  5
3  0  1  1
4  7  5  5
5  0  1  1

Alternative Methods

Using iloc[] for Position-Based Selection

Use iloc[] for integer position-based indexing ?

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]
   }
)

# Select rows by position (2 to 5, exclusive end)
selected_rows = df.iloc[2:6]
print("Selected rows using iloc[2:6]:")
print(selected_rows)

Selected rows using iloc[2:6]:
   x  y  z
2  7  5  5
3  0  1  1
4  7  5  5
5  0  1  1

Comparison

Conclusion

Use df.loc[start:end] for label-based row selection with inclusive endpoints. Use df.iloc[start:end] for position-based selection with exclusive endpoints.