eda-report 2.4.1

A simple program to automate exploratory data analysis and reporting.

eda-report - Automated Exploratory Data Analysis

A Python program to help automate the exploratory data analysis and reporting process.

Input data is analysed using pandas and SciPy. Graphs are plotted using matplotlib and seaborn. The results are then nicely packaged as a Word (.docx) document using python-docx.

Installation

You can install the package from PyPI using:

pip install eda-report

Basic Usage

1. Graphical User Interface

The eda-report command launches a graphical window to help select and analyse a csv/excel file:

eda-report

You will be prompted to set a report title, target variable (optional, for grouping values), graph color and output filename, after which the contents of the input file will be analysed, and the results will be saved in a Word (.docx) document.

NOTE: For help with Tk - related issues, consider visiting TkDocs.

2. Command Line Interface

To analyse a file named input.csv, just supply its path to the eda-report command:

eda-report -i input.csv

Or even:

eda-report -i input.csv -o output.docx -c cyan --title 'EDA Report'

For more details on the optional arguments, pass the -h or --help flag to view the help message:

eda-report -h

usage: eda-report [-h] [-i INFILE] [-o OUTFILE] [-t TITLE] [-c COLOR]
                  [-T TARGET]

Automatically analyse data and generate reports. A graphical user interface
will be launched if none of the optional arguments is specified.

optional arguments:
  -h, --help            show this help message and exit
  -i INFILE, --infile INFILE
                        A .csv or .xlsx file to analyse.
  -o OUTFILE, --outfile OUTFILE
                        The output name for analysis results (default: eda-
                        report.docx)
  -t TITLE, --title TITLE
                        The top level heading for the report (default:
                        Exploratory Data Analysis Report)
  -c COLOR, --color COLOR
                        The color to apply to graphs (default: cyan)
  -T TARGET, --target TARGET
                        The target variable (dependent feature), used to group
                        plotted values. An integer value is treated as a
                        column index, whereas a string is treated as a column
                        label.

3. Interactive Mode

3.1 Analyse data

>>> import eda_report
>>> from seaborn import load_dataset
>>> iris_data = load_dataset("iris")
>>> eda_report.summarize(iris_data)
                        OVERVIEW
                        ========
Numeric features: sepal_length, sepal_width, petal_length, petal_width
Categorical features: species
                          ***
          Summary Statistics (Numeric features)
          -------------------------------------
              count    mean     std  min  25%   50%  75%  max  skewness  kurtosis
sepal_length  150.0  5.8433  0.8281  4.3  5.1  5.80  6.4  7.9    0.3149   -0.5521
sepal_width   150.0  3.0573  0.4359  2.0  2.8  3.00  3.3  4.4    0.3190    0.2282
petal_length  150.0  3.7580  1.7653  1.0  1.6  4.35  5.1  6.9   -0.2749   -1.4021
petal_width   150.0  1.1993  0.7622  0.1  0.3  1.30  1.8  2.5   -0.1030   -1.3406
                          ***
          Summary Statistics (Categorical features)
          -----------------------------------------
        count unique     top freq relative freq
species   150      3  setosa   50        33.33%
                          ***
          Bivariate Analysis (Correlation)
          --------------------------------
sepal_length & petal_width --> strong positive correlation (0.82)
sepal_length & petal_length --> strong positive correlation (0.87)
petal_length & petal_width --> very strong positive correlation (0.96)
sepal_length & sepal_width --> very weak negative correlation (-0.12)
sepal_width & petal_width --> weak negative correlation (-0.37)
sepal_width & petal_length --> weak negative correlation (-0.43)

3.2 Generate a report

>>> eda_report.get_word_report(iris_data)
Bivariate analysis: 100%|███████████████████████████████████| 6/6 numeric pairs.
Univariate analysis: 100%|███████████████████████████████████| 5/5 features.
[INFO 17:31:37.880] Done. Results saved as 'eda-report.docx'
<eda_report.document.ReportDocument object at 0x7f3040c9bcd0>

Visit the official documentation for more details.