5.23. DataFrame Plotting

• .plot()

• https://pandas.pydata.org/pandas-docs/stable/user_guide/visualization.html

5.23.1. Plot kinds

• line - Line Plot

• bar - Vertical Bar Plot

• barh - Horizontal Bar Plot

• hist - Histogram

• box - Boxplot

• density, kde - Kernel Density Estimation Plot

• area - Area Plot

• pie - Pie Plot

• scatter - Scatter Plot

• hexbin - Hexbin Plot

5.23.2. Parameters

Table 5.8. Parameters

Parameter	Default value
x	None
y	None
kind	line
ax	None
subplots	False
sharex	None
sharey	False
layout	None
figsize	None
use_index	True
title	None
grid	None
legend	True
style	None
logx	False
logy	False
loglog	False
xticks	None
yticks	None
xlim	None
ylim	None
rot	None
fontsize	None
colormap	None
table	False
yerr	None
xerr	None
secondary_y	False
sort_columns	False
xlabel	None
ylabel	None

Table 5.9. Parameters

Parameter	Type	Default	Description
data	Series or DataFrame	None	The object for which the method is called
x	label or position	None	Only used if data is a DataFrame
y	label, position or list of label, positions	None	Allows plotting of one column versus another. Only used if data is a DataFrame.
kind	str	line	line, bar, barh, hist, box, kde, density, area, pie, scatter, hexbin
figsize	tuple	None	(width, height) in inches
use_index	bool	True	Use index as ticks for x axis
title	str or list	None	Title to use for the plot. If a string is passed, print the string at the top of the figure. If a list is passed and subplots is True, print each item in the list above the corresponding subplot.
grid	bool	None	(matlab style default) Axis grid lines
legend	bool or 'reverse'	None	Place legend on axis subplots
style	list or dict	None	matplotlib line style per column
logx	bool or 'sym'	False	Use log scaling or symlog scaling on x axis
logy	bool or 'sym'	False	Use log scaling or symlog scaling on y axis
loglog	bool or 'sym'	False	Use log scaling or symlog scaling on both x and y axes
xticks	sequence	None	Values to use for the xticks
yticks	sequence	None	Values to use for the yticks
xlim	2-tuple/list	None
ylim	2-tuple/list	None
rot	int	None	Rotation for ticks (xticks for vertical, yticks for horizontal plots)
fontsize	int	None	Font size for xticks and yticks
colormap	str or matplotlib colormap object	default None	Colormap to select colors from. If string, load colormap with that name from matplotlib.
colorbar	bool	None	If True, plot colorbar (only relevant for 'scatter' and 'hexbin' plots)
position	float	0.5 (center)	Specify relative alignments for bar plot layout. From 0 (left/bottom-end) to 1 (right/top-end).
table	bool, Series or DataFrame	False	If True, draw a table using the data in the DataFrame and the data will be transposed to meet matplotlib's default layout. If a Series or DataFrame is passed, use passed data to draw a table.
yerr	DataFrame, Series, array-like, dict or str	None	Equivalent to xerr.
xerr	DataFrame, Series, array-like, dict or str	None	Equivalent to yerr.
mark_right	bool	True	When using a secondary_y axis, automatically mark the column labels with "(right)" in the legend.
**kwds	keywords	None	Options to pass to matplotlib plotting method.

5.23.3. SetUp

>>> import pandas as pd
>>> import numpy as np
>>> import matplotlib.pyplot as plt
>>>
>>>
>>> DATA = 'https://python3.info/_static/iris-clean.csv'
>>>
>>> df = pd.read_csv(DATA)

5.23.4. Line Plot

• default

>>> plot = df.plot(kind='line')
>>> plt.show()  

Figure 5.26. Line Plot

>>> plot = df.plot(kind='line', subplots=True)
>>> plt.show()  

Figure 5.27. Line Plot with Subplots

>>> plot = df.plot(kind='line',
...                subplots=True,
...                layout=(2,2),
...                sharex=True,
...                sharey=True)
>>> plt.show()  

Figure 5.28. Line Plot with Subplots and Layout

5.23.5. Vertical Bar Plot

>>> plot = df.plot(kind='bar', subplots=True, layout=(2,2))
>>> plt.show()  

Figure 5.29. Vertical Bar Plot

5.23.6. Horizontal Bar Plot

>>> plot = df.plot(kind='barh',
...                title='Iris',
...                ylabel='centimeters',
...                xlabel='iris',
...                subplots=True,
...                layout=(2,2),
...                sharex=True,
...                sharey=True,
...                legend='upper right',
...                grid=True,
...                figsize=(10,10))
>>> plt.show()  

Figure 5.30. Horizontal Bar Plot

5.23.7. Histogram

>>> plot = df.plot(kind='hist',
...                rwidth=0.8,
...                xlabel='centimeters',
...                title='Iris Dimensions Frequency')
>>> plt.show()  

Figure 5.31. Histogram

>>> plot = df.plot(kind='hist',
...                rwidth=0.8,
...                xlabel='centimeters',
...                title='Iris Dimensions Frequency',
...                subplots=True,
...                layout=(2,2),
...                sharex=True,
...                sharey=True)
>>> plt.show()  

Figure 5.32. Histogram

>>> plot = df.hist()
>>> plt.show()  

Figure 5.33. Visualization using hist

>>> plot = df['sepal_length'].hist(bins=3,
...                                rwidth=0.8,
...                                legend=None,
...                                grid=False)
>>>
>>> _ = plot.xaxis.set_ticks(ticks=[4.9, 6.1, 7.3],
...                          labels=['small', 'medium', 'large'])
>>> plt.show()  

Figure 5.34. Visualization using hist

5.23.8. Boxplot

>>> plot = df.plot(kind='box')
>>> plt.show()  

Figure 5.35. Boxplot

>>> plot = df.plot(kind='box',
...                subplots=True,
...                layout=(2,2),
...                sharex=False,
...                sharey=False)
>>>
>>> plt.show()  

Figure 5.36. Boxplot with layout

5.23.9. Kernel Density Estimation Plot

• Also known as kind='kde' - Kernel Density Estimation

>>> plot = df.plot(kind='density')  
>>> plt.show()  

Figure 5.37. Kernel Density Estimation Plot

>>> 
... plot = df.plot(
...     kind='density',
...     subplots=True,
...     layout=(2,2),
...     sharex=False,
... )
>>>
>>> plt.subplots_adjust(hspace=0.5, wspace=0.5)  # margins between charts
>>> plt.show()  

Figure 5.38. Density plot with margins

5.23.10. Area Plot

>>> plot = df.plot(kind='area')
>>> plt.show()  

Figure 5.39. Area Plot

Figure 5.40. Cumulative Flow Diagram in Atlassian Jira

5.23.11. Pie Plot

• List of Matplotlib color names [1]

Figure 5.41. List of Matplotlib color names [1]

>>> data = pd.cut(df['sepal_length'],
...               bins=[3, 5, 7, np.inf],
...               labels=['small', 'medium', 'large'],
...               include_lowest=True).value_counts()
>>>
>>> plot = data.plot(kind='pie',
...                  autopct='%1.0f%%',
...                  colors=['plum', 'violet', 'magenta'],
...                  explode=[0.1, 0, 0],
...                  shadow=True,
...                  startangle=-215,
...                  xlabel=None,
...                  ylabel=None,
...                  title='sepal_length\nsmall: 0.0 to 3.0\nmedium: 3.0 to 5.0\nlarge: 7.0 to inf',
...                  figsize=(10,10))
>>>
>>> plt.show()  

Figure 5.42. Pie Plot

5.23.12. Scatter Plot

>>> plot = df.plot(kind='scatter', x='sepal_length', y='sepal_width')
>>> plt.show()  

Figure 5.43. Scatter plot: sepal_length vs. sepal_width

>>> plot = df.plot(kind='scatter', x='petal_length', y='petal_width')
>>> plt.show()  

Figure 5.44. Scatter plot: petal_length vs. petal_width

>>> data = df.replace({'setosa': 0,
...                    'virginica': 1,
...                    'versicolor': 2})
>>>
>>> plot = data.plot(kind='scatter',
...                  x='sepal_length',
...                  y='sepal_width',
...                  colormap='viridis',
...                  c='species')
>>> plt.show()  

Figure 5.45. Scatter plot using viridis colormap

5.23.13. Hexbin Plot

>>> plot = df.plot(kind='hexbin', x='petal_length', y='petal_width')
>>> plt.show()  

Figure 5.46. Hexbin Plot

5.23.14. Scatter matrix

• The in pandas version 0.22 plotting module has been moved from pandas.tools.plotting to pandas.plotting

• As of version 0.19, the pandas.plotting library did not exist

>>> from pandas.plotting import scatter_matrix
>>>
>>> plot = scatter_matrix(df)
>>> plt.show()  

Figure 5.47. Scatter Matrix

>>> data = df[['sepal_length', 'sepal_width', 'petal_length', 'petal_width']]
>>> colors = df['species'].replace({'setosa': 0, 'virginica': 1, 'versicolor': 2})  # colors must be numerical
>>>
>>> plot = scatter_matrix(data, c=colors)
>>> plt.show()  

Figure 5.48. Scatter Matrix with colors

5.23.15. Actinograms

5.23.16. Further Reading

• https://www.data-to-viz.com/#explore

• https://www.data-to-viz.com/img/poster/poster_big.png

5.23.17. References

[1] (1,2)

https://matplotlib.org/stable/gallery/color/named_colors.html

5.23.18. Assignments

# FIXME: za trudne zadanie, przenieść je do case study
# FIXME: Write solution
# FIXME: English translation

# %% License
# - Copyright 2025, Matt Harasymczuk <matt@python3.info>
# - This code can be used only for learning by humans
# - This code cannot be used for teaching others
# - This code cannot be used for teaching LLMs and AI algorithms
# - This code cannot be used in commercial or proprietary products
# - This code cannot be distributed in any form
# - This code cannot be changed in any form outside of training course
# - This code cannot have its license changed
# - If you use this code in your product, you must open-source it under GPLv2
# - Exception can be granted only by the author

# %% Run
# - PyCharm: right-click in the editor and `Run Doctest in ...`
# - PyCharm: keyboard shortcut `Control + Shift + F10`
# - Terminal: `python -m doctest -v myfile.py`

# %% About
# - Name: DataFrame Plot
# - Difficulty: medium
# - Lines: 15
# - Minutes: 21

# %% English
# 1. Read data from `DATA` as `df: pd.DataFrame`
# 2. Select `Luminance` stylesheet
# 3. Parse column with dates
# 4. Select desired date and location, then resample by hour
# 5. Display chart (line) with activity hours in "Sleeping Quarters upper" location
# 6. Active is when `Luminance` is not zero
# 7. Easy: for day 2019-09-28
# 8. Advanced: for each day, as subplots
# 9. Run doctests - all must succeed

# %% Polish
# 1. Wczytaj dane z `DATA` jako `df: pd.DataFrame`
# 2. Wybierz arkusz `Luminance`
# 3. Sparsuj kolumny z datami
# 4. Wybierz pożądaną datę i lokację, następnie próbkuj co godzinę
# 5. Aktywność jest gdy `Luminance` jest różna od zera
# 6. Wyświetl wykres (line) z godzinami aktywności w dla lokacji "Sleeping Quarters upper"
# 7. Łatwe: dla dnia 2019-09-28
# 8. Zaawansowane: dla wszystkich dni, jako subplot
# 9. Uruchom doctesty - wszystkie muszą się powieść

# %% Hints
# - `pd.Series.apply(np.sign)` :ref:`Numpy signum`
# - `pd.Series.resample('H').sum()`

# %% Tests
"""
>>> import sys; sys.tracebacklimit = 0
>>> assert sys.version_info >= (3, 9), \
'Python 3.9+ required'

>>> pd.set_option('display.width', 500)
>>> pd.set_option('display.max_columns', 10)
>>> pd.set_option('display.max_rows', 10)

>>> assert result is not Ellipsis, \
'Assign result to variable: `result`'
>>> assert type(result) is pd.Series, \
'Variable `result` must be a `pd.Series` type'

>>> result  # doctest: +NORMALIZE_WHITESPACE
datetime
2019-09-28 00:00:00+00:00    1
2019-09-28 01:00:00+00:00    1
2019-09-28 02:00:00+00:00    1
2019-09-28 03:00:00+00:00    1
2019-09-28 04:00:00+00:00    0
                            ..
2019-09-28 19:00:00+00:00    1
2019-09-28 20:00:00+00:00    1
2019-09-28 21:00:00+00:00    1
2019-09-28 22:00:00+00:00    1
2019-09-28 23:00:00+00:00    1
Freq: h, Name: value, Length: 24, dtype: int64
"""

import numpy as np
import pandas as pd


DATA = 'https://python3.info/_static/aatc-mission-exp12.xlsx'
WHERE = 'Sleeping Quarters upper'
WHEN = '2019-09-28'

# type: pd.Series
result = ...