Split Pandas Dataframe by Rows

Split Pandas Dataframe by Rows

Splitting a pandas DataFrame by rows can be achieved using various methods depending on your specific needs. Here are a few common use cases and methods to split a DataFrame by rows:

  1. Splitting DataFrame into Two Parts

    If you want to split a DataFrame into two parts, you can use the .iloc method:

    import pandas as pd # Sample DataFrame df = pd.DataFrame({'A': [1, 2, 3, 4, 5], 'B': [10, 20, 30, 40, 50]}) # Splitting DataFrame into two parts df1 = df.iloc[:3] # first 3 rows df2 = df.iloc[3:] # rest of the rows print(df1) print(df2)

  2. Splitting DataFrame into Multiple Parts

    If you want to split the DataFrame into multiple parts (e.g., 3 equal parts), you can calculate the size of each split and then use .iloc:

    num_splits = 3 split_size = len(df) // num_splits dfs = [df.iloc[i*split_size: (i+1)*split_size] for i in range(num_splits)]

    Now dfs is a list containing the three split DataFrames.

  3. Splitting DataFrame Using a Condition

    If you want to split a DataFrame based on a specific condition:

    # Condition where 'A' is less than 4 condition = df['A'] < 4 df_true = df[condition] df_false = df[~condition] print(df_true) print(df_false)

  4. Randomly Splitting DataFrame

    If you want to split a DataFrame into two parts randomly, you can use the train_test_split method from sklearn:

    from sklearn.model_selection import train_test_split # Split DataFrame into 70% train and 30% test df_train, df_test = train_test_split(df, test_size=0.3)

  5. Splitting DataFrame by Groups

    If you have a column that you want to use to group the DataFrame and then split based on those groups:

    grouped = df.groupby('column_name') dfs = [group for _, group in grouped]

    Now dfs is a list where each element is a DataFrame corresponding to a unique value in column_name.

Select the method that best fits your use case to split your DataFrame by rows.

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