python - How to select percentage of rows in pandas dataframe

Python - How to select percentage of rows in pandas dataframe

To select a percentage of rows in a Pandas DataFrame, you can use the sample method along with the frac parameter. Here's an example:

import pandas as pd # Create a sample DataFrame data = {'A': [1, 2, 3, 4, 5], 'B': [6, 7, 8, 9, 10], 'C': [11, 12, 13, 14, 15]} df = pd.DataFrame(data) # Specify the percentage of rows to select (e.g., 30%) percentage_to_select = 0.3 # Select a percentage of rows selected_rows = df.sample(frac=percentage_to_select, random_state=42) print("Original DataFrame:") print(df) print("\nSelected Rows:") print(selected_rows)

In this example, df.sample(frac=percentage_to_select, random_state=42) selects a random fraction of rows from the DataFrame. The random_state parameter is used for reproducibility.

Adjust the value of percentage_to_select based on your requirements. Keep in mind that the sample method randomly selects rows, so the selected rows may vary each time you run the code. If you need a consistent subset, set the random_state parameter to a fixed value.

Examples

  1. Select a Random Percentage of Rows in a Pandas DataFrame Using sample:

    import pandas as pd df = pd.read_csv('your_dataset.csv') # Select 20% random rows percentage = 20 selected_rows = df.sample(frac=percentage/100)

    Description: Use the sample method to randomly select a percentage of rows (e.g., 20%) from a Pandas DataFrame.

  2. Select the Top Percentage of Rows Based on a Column in Pandas Using nlargest:

    import pandas as pd df = pd.read_csv('your_dataset.csv') # Select top 30% based on a specific column (e.g., 'column_name') percentage = 30 selected_rows = df.nlargest(int(len(df) * percentage / 100), 'column_name')

    Description: Use nlargest to select the top percentage of rows based on a specific column (e.g., 'column_name') in a Pandas DataFrame.

  3. Select a Fixed Percentage of the First Rows in a Pandas DataFrame:

    import pandas as pd df = pd.read_csv('your_dataset.csv') # Select the first 15% of rows percentage = 15 selected_rows = df.head(int(len(df) * percentage / 100))

    Description: Use head to select a fixed percentage of the first rows (e.g., 15%) in a Pandas DataFrame.

  4. Select a Percentage of Rows Based on a Condition Using query:

    import pandas as pd df = pd.read_csv('your_dataset.csv') # Select 25% of rows where a specific condition is met (e.g., 'column_name > 50') percentage = 25 selected_rows = df.query('column_name > 50').sample(frac=percentage/100)

    Description: Use query to filter rows based on a condition (e.g., 'column_name > 50') and then sample a percentage (e.g., 25%) from the result.

  5. Select a Percentage of Unique Rows Based on a Column in Pandas Using drop_duplicates:

    import pandas as pd df = pd.read_csv('your_dataset.csv') # Select 10% of unique rows based on a specific column (e.g., 'column_name') percentage = 10 selected_rows = df.drop_duplicates('column_name').sample(frac=percentage/100)

    Description: Use drop_duplicates to select a percentage of unique rows based on a specific column (e.g., 'column_name').

  6. Select a Random Percentage of Rows Within a Group in Pandas Using groupby and apply:

    import pandas as pd df = pd.read_csv('your_dataset.csv') # Select 15% random rows within each group based on a specific column (e.g., 'group_column') percentage = 15 selected_rows = df.groupby('group_column').apply(lambda x: x.sample(frac=percentage/100)).reset_index(drop=True)

    Description: Use groupby and apply to select a random percentage of rows within each group based on a specific column (e.g., 'group_column').

  7. Select a Percentage of Rows After Sorting in Pandas Using sort_values:

    import pandas as pd df = pd.read_csv('your_dataset.csv') # Select 12% of rows after sorting based on a specific column (e.g., 'sort_column') percentage = 12 selected_rows = df.sort_values(by='sort_column').head(int(len(df) * percentage / 100))

    Description: Use sort_values to sort the DataFrame based on a specific column (e.g., 'sort_column') and then select a percentage (e.g., 12%) of the rows.

  8. Select a Percentage of Rows with Stratified Sampling in Pandas Using StratifiedShuffleSplit:

    import pandas as pd from sklearn.model_selection import StratifiedShuffleSplit df = pd.read_csv('your_dataset.csv') # Select 18% of rows with stratified sampling based on a specific column (e.g., 'stratify_column') percentage = 18 splitter = StratifiedShuffleSplit(n_splits=1, test_size=percentage/100) _, indices = next(splitter.split(df, df['stratify_column'])) selected_rows = df.iloc[indices]

    Description: Use StratifiedShuffleSplit from scikit-learn to perform stratified sampling and select a percentage (e.g., 18%) of rows based on a specific column (e.g., 'stratify_column').

  9. Select a Random Percentage of Rows Without Replacement in Pandas Using sample:

    import pandas as pd df = pd.read_csv('your_dataset.csv') # Select 8% random rows without replacement percentage = 8 selected_rows = df.sample(frac=percentage/100, replace=False)

    Description: Use sample to randomly select a percentage (e.g., 8%) of rows without replacement from a Pandas DataFrame.

  10. Select a Percentage of Rows Based on a Weighted Sampling in Pandas Using numpy.random.choice:

    import pandas as pd import numpy as np df = pd.read_csv('your_dataset.csv') # Select 5% of rows with weighted sampling based on a specific column (e.g., 'weights_column') percentage = 5 weights = df['weights_column'] selected_indices = np.random.choice(df.index, size=int(len(df) * percentage / 100), replace=False, p=weights / weights.sum()) selected_rows = df.loc[selected_indices]

    Description: Use numpy.random.choice to perform weighted sampling and select a percentage (e.g., 5%) of rows based on a specific column (e.g., 'weights_column').

More Tags

mongodb-aggregation decode connection-pooling user-permissions greatest-common-divisor android-arrayadapter web-testing reboot void-pointers r-rownames

More Programming Questions

More Electronics Circuits Calculators

More Date and Time Calculators

More Mortgage and Real Estate Calculators

More Mixtures and solutions Calculators