plot(): analyze distributions

Overview

The function plot() explores the distributions and statistics of the dataset. It generates a variety of visualizations and statistics which enables the user to achieve a comprehensive understanding of the column distributions and their relationships. The following describes the functionality of plot() for a given dataframe df.

1. plot(df): plots the distribution of each column and computes dataset statistics

2. plot(df, col1): plots the distribution of column col1 in various ways, and computes its statistics

3. plot(df, col1, col2): generates plots depicting the relationship between columns col1 and col2

The generated plots are different for numerical, categorical and geography columns. The following table summarizes the output for the different column types.

col1	col2	Output
None	None	dataset statistics, histogram or bar chart for each column
Numerical	None	column statistics, histogram, kde plot, qq-normal plot, box plot
Categorical	None	column statistics, bar chart, pie chart, word cloud, word frequencies
Geography	None	column statistics, bar chart, pie chart, word cloud, word frequencies, world map
Numerical	Numerical	scatter plot, hexbin plot, binned box plot
Numerical	Categorical	categorical box plot, multi-line chart
Categorical	Numerical	categorical box plot, multi-line chart
Categorical	Categorical	nested bar chart, stacked bar chart, heat map
Categorical	Geography	nested bar chart, stacked bar chart, heat map
Geography	Categorical	nested bar chart, stacked bar chart, heat map
Geopoint	Categorical	nested bar chart, stacked bar chart, heat map
Categorical	Geopoint	nested bar chart, stacked bar chart, heat map
Numerical	Geography	categorical box plot, multi-line chart, world map
Geography	Numerical	categorical box plot, multi-line chart, world map
Numerical	Geopoint	geo map
Geopoint	Numerical	geo map

Next, we demonstrate the functionality of plot().

Load the dataset

dataprep.eda supports Pandas and Dask dataframes. Here, we will load the well-known adult dataset into a Pandas dataframe using the load_dataset function.

from dataprep.datasets import load_dataset
import numpy as np
df = load_dataset('adult')
df = df.replace(" ?", np.NaN)

Get an overview of the dataset with plot(df)

We start by calling plot(df) which computes dataset-level statistics, a histogram for each numerical column, and a bar chart for each categorical column. The number of bins in the histogram can be specified with the parameter bins, and the number of categories in the bar chart can be specified with the parameter ngroups. If a column contains missing values, the percent of missing values is shown in the title and ignored when generating the plots.

from dataprep.eda import plot
plot(df)

Stats and Insights

Dataset Statistics

Number of Variables	15
Number of Rows	48842
Missing Cells	6465
Missing Cells (%)	0.9%
Duplicate Rows	52
Duplicate Rows (%)	0.1%
Total Size in Memory	30.1 MB
Average Row Size in Memory	645.7 B
Variable Types	Numerical: 6 Categorical: 9

Dataset Insights

workclass has 2799 (5.73%) missing values	Missing
occupation has 2809 (5.75%) missing values	Missing
native-country has 857 (1.75%) missing values	Missing
fnlwgt is skewed	Skewed
education-num is skewed	Skewed
capital-gain is skewed	Skewed
capital-loss is skewed	Skewed
hours-per-week is skewed	Skewed
capital-gain has 44807 (91.74%) zeros	Zeros
capital-loss has 46560 (95.33%) zeros	Zeros

age

'hist.bins': 50

Number of bins in the histogram

'hist.yscale': 'linear'

Y-axis scale ("linear" or "log")

'hist.color': '#aec7e8'

Color

Copy All Parameters

workclass

'bar.bars': 10

Maximum number of bars to display

'bar.sort_descending': True

Whether to sort the bars in descending order

'bar.yscale': 'linear'

Y-axis scale ("linear" or "log")

Copy All Parameters

Column Insights

1. workclass has 2799 (5.73%) missing values

fnlwgt

'hist.bins': 50

Number of bins in the histogram

'hist.yscale': 'linear'

Y-axis scale ("linear" or "log")

'hist.color': '#aec7e8'

Color

Copy All Parameters

Column Insights

1. fnlwgt is skewed

education

'bar.bars': 10

Maximum number of bars to display

'bar.sort_descending': True

Whether to sort the bars in descending order

'bar.yscale': 'linear'

Y-axis scale ("linear" or "log")

Copy All Parameters

education-num

'hist.bins': 50

Number of bins in the histogram

'hist.yscale': 'linear'

Y-axis scale ("linear" or "log")

'hist.color': '#aec7e8'

Color

Copy All Parameters

Column Insights

1. education-num is skewed

marital-status

'bar.bars': 10

Maximum number of bars to display

'bar.sort_descending': True

Whether to sort the bars in descending order

'bar.yscale': 'linear'

Y-axis scale ("linear" or "log")

Copy All Parameters

occupation

'bar.bars': 10

Maximum number of bars to display

'bar.sort_descending': True

Whether to sort the bars in descending order

'bar.yscale': 'linear'

Y-axis scale ("linear" or "log")

Copy All Parameters

Column Insights

1. occupation has 2809 (5.75%) missing values

relationship

'bar.bars': 10

Maximum number of bars to display

'bar.sort_descending': True

Whether to sort the bars in descending order

'bar.yscale': 'linear'

Y-axis scale ("linear" or "log")

Copy All Parameters

race

'bar.bars': 10

Maximum number of bars to display

'bar.sort_descending': True

Whether to sort the bars in descending order

'bar.yscale': 'linear'

Y-axis scale ("linear" or "log")

Copy All Parameters

sex

'bar.bars': 10

Maximum number of bars to display

'bar.sort_descending': True

Whether to sort the bars in descending order

'bar.yscale': 'linear'

Y-axis scale ("linear" or "log")

Copy All Parameters

capital-gain

'hist.bins': 50

Number of bins in the histogram

'hist.yscale': 'linear'

Y-axis scale ("linear" or "log")

'hist.color': '#aec7e8'

Color

Copy All Parameters

Column Insights

1. capital-gain is skewed
2. capital-gain has 44807 (91.74%) zeros

capital-loss

'hist.bins': 50

Number of bins in the histogram

'hist.yscale': 'linear'

Y-axis scale ("linear" or "log")

'hist.color': '#aec7e8'

Color

Copy All Parameters

Column Insights

1. capital-loss is skewed
2. capital-loss has 46560 (95.33%) zeros

hours-per-week

'hist.bins': 50

Number of bins in the histogram

'hist.yscale': 'linear'

Y-axis scale ("linear" or "log")

'hist.color': '#aec7e8'

Color

Copy All Parameters

Column Insights

1. hours-per-week is skewed

native-country

'bar.bars': 10

Maximum number of bars to display

'bar.sort_descending': True

Whether to sort the bars in descending order

'bar.yscale': 'linear'

Y-axis scale ("linear" or "log")

Copy All Parameters

Column Insights

1. native-country has 857 (1.75%) missing values

class

'bar.bars': 10

Maximum number of bars to display

'bar.sort_descending': True

Whether to sort the bars in descending order

'bar.yscale': 'linear'

Y-axis scale ("linear" or "log")

Copy All Parameters

Understand a column with plot(df, col1)

After getting an overview of the dataset, we can thoroughly investigate a column of interest col1 using plot(df, col1). The output is of plot(df, col1) is different for numerical and categorical columns.

When col1 is a numerical column, it computes column statistics, and generates a histogram, kde plot, box plot and qq-normal plot:

plot(df, "age")

Stats Histogram KDE Plot Normal Q-Q Plot Box Plot Value Table

Overview

Approximate Distinct Count	74
Approximate Unique (%)	0.2%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Memory Size	763.2 KB
Mean	38.6436
Minimum	17
Maximum	90
Zeros	0
Zeros (%)	0.0%
Negatives	0
Negatives (%)	0.0%

Quantile Statistics

Minimum	17
5-th Percentile	19
Q1	28
Median	37
Q3	48
95-th Percentile	63
Maximum	90
Range	73
IQR	20

Descriptive Statistics

Mean	38.6436
Standard Deviation	13.7105
Variance	187.9781
Sum	1.8874e+06
Skewness	0.5576
Kurtosis	-0.1844
Coefficient of Variation	0.3548

'hist.bins': 50

Number of bins in the histogram

'hist.yscale': 'linear'

Y-axis scale ("linear" or "log")

'hist.color': '#aec7e8'

Color

'height': 400

Height of the plot

'width': 450

Width of the plot

Copy All Parameters

• age is skewed right (γ1 = 0.5576)

'kde.bins': 50

Number of bins in the histogram

'kde.yscale': 'linear'

Y-axis scale ("linear" or "log")

'kde.hist_color': '#aec7e8'

Color of the density histogram

'kde.line_color': '#d62728'

Color of the density line

'height': 400

Height of the plot

'width': 450

Width of the plot

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'qqnorm.point_color': #1f77b4

Color of the points

'qqnorm.line_color': #d62728

Color of the line

'height': 400

Height of the plot

'width': 450

Width of the plot

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'box.color': #1f77b4

Color

'height': 400

Height of the plot

'width': 450

Width of the plot

Copy All Parameters

• age has 216 outliers

'value_table.ngroups': 10

The number of distinct values to show

Copy All Parameters

Value	Count	Frequency (%)
36	1348	2.8%
35	1337	2.7%
33	1335	2.7%
23	1329	2.7%
31	1325	2.7%
34	1303	2.7%
28	1280	2.6%
37	1280	2.6%
30	1278	2.6%
38	1264	2.6%
Other values (64)	35763	73.2%

When x is a categorical column, it computes column statistics, and plots a bar chart, pie chart, word cloud, word frequency and word length:

plot(df, "education")