eda-report 2.7.3

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 analyzed using pandas and SciPy. Graphs are plotted using matplotlib. 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 analyze a csv/excel file:

eda-report

You will be prompted to set a report title, group-by variable (optional), graph color and output filename, after which the contents of the input file will be analyzed, 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 analyze 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]
                  [-g GROUPBY]

Automatically analyze 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 analyze.
  -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)
  -g GROUPBY, -T GROUPBY, --groupby GROUPBY, --target GROUPBY
                        The variable to use for grouping 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 Analyze data

>>> 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%

          Pearson's Correlation (Top 20)
          ------------------------------
petal_length & petal_width --> very strong positive correlation (0.96)
sepal_length & petal_length --> very strong positive correlation (0.87)
sepal_length & petal_width --> very strong positive correlation (0.82)
sepal_width & petal_length --> moderate negative correlation (-0.43)
sepal_width & petal_width --> weak negative correlation (-0.37)
sepal_length & sepal_width --> very weak negative correlation (-0.12)

3.2 Plot statistical graphs

>>> fig = ep.regression_plot(mpg_data["acceleration"], mpg_data["horsepower"],
...                          labels=("Acceleration", "Horsepower"))
>>> fig.savefig("regression-plot.png")

3.3 Generate a report

>>> eda_report.get_word_report(iris_data)
Analyze variables:  100%|███████████████████████████████████| 5/5
Plot variables:     100%|███████████████████████████████████| 5/5
Bivariate analysis: 100%|███████████████████████████████████| 6/6 pairs.
[INFO 16:14:53.648] Done. Results saved as 'eda-report.docx'
<eda_report.document.ReportDocument object at 0x7f196753bd60>

Visit the official documentation for more info.