Quickstart#
Using the Graphical User Interface#
The command eda-report launches a graphical window to help select a csv or excel file to analyze:
$ eda-report
A tkinter-based graphical user interface to the application#
You will be prompted to enter your desired title, groupby/target variable, graph color & output file-name. Afterwards, a report is generated, as specified, from the contents of the selected file.
Hint
For help with Tk - related issues, consider visiting TkDocs.
Using the Command Line Interface#
You can specify an input file and an output file-name:
$ eda-report -i data.csv -o some_name.docx
$ 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.
From an Interactive Session#
You can use the get_word_report() function to generate reports:
>>> import eda_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>
You can use the summarize() function to analyze datasets:
>>> eda_report.summarize(range(50))
Name: var_1
Type: numeric
Non-null Observations: 50
Unique Values: 50 -> [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, [...]
Missing Values: None
Summary Statistics
------------------
Average: 24.5000
Standard Deviation: 14.5774
Minimum: 0.0000
Lower Quartile: 12.2500
Median: 24.5000
Upper Quartile: 36.7500
Maximum: 49.0000
Skewness: 0.0000
Kurtosis: -1.2000
Tests for Normality
-------------------
p-value Conclusion at α = 0.05
D'Agostino's K-squared test 0.0015981 Unlikely to be normal
Kolmogorov-Smirnov test 0.0000000 Unlikely to be normal
Shapiro-Wilk test 0.0580895 Possibly normal
>>> eda_report.summarize(iris_data)
Summary Statistics for Numeric features (4)
-------------------------------------------
count avg stddev min 25% 50% 75% max skewness kurtosis
sepal_length 150 5.8433 0.8281 4.3 5.1 5.80 6.4 7.9 0.3149 -0.5521
sepal_width 150 3.0573 0.4359 2.0 2.8 3.00 3.3 4.4 0.3190 0.2282
petal_length 150 3.7580 1.7653 1.0 1.6 4.35 5.1 6.9 -0.2749 -1.4021
petal_width 150 1.1993 0.7622 0.1 0.3 1.30 1.8 2.5 -0.1030 -1.3406
Summary Statistics for Categorical features (1)
-----------------------------------------------
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)
You can plot several statistical graphs (see Plotting Examples):
>>> import eda_report.plotting as ep
>>> ax = ep.plot_correlation(mpg_data)
>>> ax.figure.savefig("correlation-plot.png")
