explore_df()

datasafari.explorer.explore_df(
df: DataFrame,
method: str = 'all',
output: str = 'print',
**kwargs,
) → str | None[source]

Explore a DataFrame and gain a birds-eye view of summary statistics, NAs, data types and more.

The function combines the most common data exploration functions in one convenient output in your console.

Parameters:

dfpandas.DataFrame

DataFrame to be explored.

methodstr, optional, default: ‘all’
Specifies the method to apply on the DataFrame.

  • 'na' Displays counts of NAs per column and percentage of NAs.

  • 'desc' Shows summary statistics using the describe method.

  • 'head' Outputs the first few rows using head.

  • 'info' Provides concise information about the DataFrame using info.

  • 'all' Executes all the above methods sequentially.

outputstr, optional, default: ‘print’
Determines the output of the exploration results.

  • 'print' Prints the results to the console.

  • 'return' Returns the results as a string.

**kwargsdict

Additional arguments for pandas methods (e.g., 'percentiles' for 'desc'). You can specify arguments applicable when ‘method’ is set to 'all', which will be appropriately directed to each pandas method used. Note that the 'buf' parameter in the 'info' method is disabled and cannot be used.

Return:

str or None

  • str If output=’return’, a string containing the formatted exploration results is returned as a uniform string.

  • None If output=’print’, results are printed to the console, and the function returns None.

Raises:

TypeErrors:

  • If df is not a pandas DataFrame.

  • If method is not a string.

  • If output is not a string.

ValueErrors:

  • If df is empty.

  • If method is not one of the valid options:.

  • If output is not ‘print’ or ‘return’.

  • If ‘buf’ parameter is used in the ‘info’ method.

Examples:

Create a sample DataFrame to use in the examples:

>>> import datasafari
>>> import numpy as np
>>> import pandas as pd
>>> data = {
...    'A': np.random.randn(100),
...    'B': np.random.rand(100) * 100,
...    'C': np.random.randint(1, 100, size=100),
...    'D': np.random.choice(['X', 'Y', 'Z'], size=100)
... }
>>> df = pd.DataFrame(data)

The full potential of explore_df() is unlocked by simply providing a dataframe:

>>> explore_df(df)

Alternatively, save the output to a string:

>>> summary = explore_df(df, 'all', output='return')

Display summary statistics with custom percentiles:

>>> explore_df(df, 'desc', percentiles=[0.05, 0.95], output='print')

Show the first 3 rows of the DataFrame:

>>> explore_df(df, 'head', n=3, output='print')

Provide detailed DataFrame information:

>>> explore_df(df, 'info', verbose=True, output='print')

Calculate and display the count and percentage of missing values:

>>> explore_df(df, 'na', output='print')

Execute a comprehensive exploration with custom settings:

>>> explore_df(df, 'all', n=3, percentiles=[0.25, 0.75], output='print')

Return comprehensive exploration results as a string:

>>> result_str = explore_df(df, 'all', n=5, output='return')
>>> print(result_str)

Use ‘all’ with kwargs applicable to specific methods, print the results:

>>> explore_df(df, 'all', n=5, percentiles=[0.1, 0.9], verbose=False, output='print')