How to Select Range Of Rows In Pandas Dataframe?

To select a range of rows in a pandas dataframe, you can use the slicing notation with square brackets. For example, to select rows 5 to 10, you can use df.iloc[5:11]. This will select rows 5, 6, 7, 8, 9, and 10. Alternatively, you can also use df.loc[] to select rows based on labels or conditions. Make sure to specify the start and end indices correctly to select the desired range of rows.

What is the sample function in pandas dataframe for selecting rows?

The sample() function in pandas dataframe is used to randomly select rows from the dataframe.

Syntax:

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df.sample(n, replace=False, random_state=None)

Parameters:

• n: Number of rows to be selected.
• replace: Whether the same row can be selected multiple times (True) or not (False).
• random_state: Seed for the random number generator.

Example:

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import pandas as pd

data = {'A': [1, 2, 3, 4, 5],
        'B': ['a', 'b', 'c', 'd', 'e']}
df = pd.DataFrame(data)

sampled_rows = df.sample(n=3)  # Selecting 3 random rows
print(sampled_rows)

This code will randomly select 3 rows from the dataframe df and print them.

How to select rows by position in a pandas dataframe?

You can select rows by position in a pandas dataframe using the iloc method. Here's an example:

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import pandas as pd

# Create a sample dataframe
data = {'A': [1, 2, 3, 4, 5],
        'B': ['a', 'b', 'c', 'd', 'e']}
df = pd.DataFrame(data)

# Select the first row by position
first_row = df.iloc[0]
print(first_row)

# Select a range of rows by position
range_of_rows = df.iloc[1:3]
print(range_of_rows)

In this example, iloc[0] selects the first row of the dataframe, while iloc[1:3] selects rows 1 and 2.

What is the iloc function in pandas dataframe for selecting rows?

The iloc function in Pandas DataFrame is used for selecting rows and columns by their position (index). It stands for "integer location" and can be used to select rows by their row number.

Syntax: df.iloc[row_number]

For example, to select the first row in a DataFrame called df, you can use:

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first_row = df.iloc[0]