How can I count rows in multiple DataFrames in Python using Jupyter?

To count rows in multiple DataFrames in Python using Jupyter, you can iterate through a list or dictionary of DataFrames and use the len() function or the .shape[0] attribute. Here's how you can do it:

1. Using a List of DataFrames:

- If you have your DataFrames stored in a list, you can easily iterate through them.

- Example code:

import pandas as pd

# Assume you have multiple DataFrames in a list
df1 = pd.DataFrame({'A': [1, 2], 'B': [3, 4]})
df2 = pd.DataFrame({'C': [5, 6, 7], 'D': [8, 9, 10]})
dataframes = [df1, df2]

for i, df in enumerate(dataframes):
  row_count = len(df)
  print(f"DataFrame {i+1} has {row_count} rows")

2. Using a Dictionary of DataFrames:

- If your DataFrames are stored as values in a dictionary, use the .items() method to iterate through key-value pairs.

- Example code:

import pandas as pd

# Assume you have multiple DataFrames in a dictionary
df_dict = {
  'df1': pd.DataFrame({'A': [1, 2], 'B': [3, 4]}),
  'df2': pd.DataFrame({'C': [5, 6, 7], 'D': [8, 9, 10]})
}

for name, df in df_dict.items():
  row_count = df.shape[0]
  print(f"DataFrame '{name}' has {row_count} rows")

3. Explanation:

- len(df) returns the number of rows in the DataFrame, which you can assign to a variable like row_count for further use.

- Alternatively, df.shape[0] also returns the number of rows in the DataFrame, as the shape attribute provides a tuple of (rows, columns).

4. Jupyter Notebook Output:

- When you run the above code in a Jupyter Notebook cell, the output will display the number of rows for each DataFrame.

5. Best Practices:

- Using .shape[0] is generally faster than len(df), so it's the preferred approach for large datasets.

By following these steps, you can efficiently count the rows in multiple DataFrames within a Python Jupyter Notebook environment. Choose the method that best suits how your DataFrames are stored.

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