11. Pandas Visualization

worksheet in Colab

Pandas offers a simple, high-level interface for creating plots directly from Series and DataFrame objects. It builds on Matplotlib behind the scenes, allowing you to make quick, readable plots with calls like df.plot() or ser.plot(). You can easily produce line, bar/stacked bar, area, scatter, box, histogram, KDE, hexbin, and pie charts while using your index (including datetime) and column labels.

In this section, you will learn:

1. Plotting*: Plotting different plots using either the df.plot() method with the kind parameter or the direct plotting methods. For example, for histograms:

   1. df.plot.hist(): Uses Pandas’ unified .plot() interface (which wraps Matplotlib) to draw a histogram.

   2. df.plot(kind='hist'): The same as df.plot.hist(); a generic form of the same call.

   3. Pandas has two top-level plotting methods: df.hist() (a quick wrapper around Matplotlib’s plt.hist() for all numeric columns) and df.boxplot().

Method	Applies to	Subplots	Overlays Multiple Columns	Based On	Typical Use
df.hist()	DataFrame only	Yes (grid of plots)	No	plt.hist()	Quick overview of all numeric columns
df.plot.hist()	DataFrame or Series	Single plot	Yes	Pandas .plot() wrapper	Custom, combined histograms

2. Custom parameters: Most plotting calls accept Matplotlib keyword arguments and return a Matplotlib Axes object for further customization. Often used parameters from pandas API reference (pandas.DataFrame.plot ) include:

   1. data: Series or DataFrame. The object for which the method is called.

   2. x: label or position, default None. Only used if data is a DataFrame.

   3. y: label, position or list of label, positions, default None. Allows plotting of one column versus another. Only used if data is a DataFrame.

   4. kind: str. The kind of plot to produce: line (default), bar, barh, hist, box, kde/density, area, pie, scatter (DataFrame only), hexbin (DataFrame only).

   5. ax

   6. figsize

   7. use_index

   8. title

   9. ticks (xticks and yticks): sequence. Values to use for ticks.

   10. lim (xlim and ylim): 2-tuple/list. Set the limits of the axes.

   11. label (xlabel and ylabel: Name to use for the label on axis (default to index name).

   12. coloarmap: str or matplotlib colormap object

   13. stacked: bool, default False in line and bar plots, and True in area plot. If True, create stacked plot.

   14. **kwargs (matplotlib ):

       1. lw

       2. alpha

3. style sheets: Style the plots to look globally with plt.style.use(...).

4. color & size:

   • For scatter plots you can color by a column (c) or

   • size by an array (s=df['col']*scale) and

   • use cmap for colormaps.

5. fine-tuning: For large or dense data, consider hexbin or KDE plots instead of scatter.

6. Plot Types The pandas.DataFrame.plot API lists 11 plots to be used with the kind parameter: The kind of plot to produce: line(default), bar, barh (horizontal bar plot), hist, box, kde (density), area, pie, scatter, hexbin. These plots can be summarized as:

Plot Type	Method	Description	Best For
area	.plot.area()	Filled area under curve	Cumulative trends
bar	.plot.bar()	Vertical bars	Category comparisons
barh	.plot.barh()	Horizontal bars	Long category labels
box	.plot.box()	Box-and-whisker plot	Distribution + outliers
density/kde	.plot.density()/.plot.kde()	Kernel density estimate	Smoothed distribution
hexbin	.plot.hexbin(x='col1', y='col2')	Hexagonal binning of points	Dense scatter data (DataFrame only)
hist	.plot.hist()	Histogram	Distribution of a numeric variable
line	.plot.line()	Default; connects data points with lines	Time series, trends
pie	.plot.pie()	Pie chart	Part-to-whole breakdown (Series only)
scatter	.plot.scatter(x='col1', y='col2')	Dots for two variables	Correlations (DataFrame only)
bar_stack	.plot.bar(stacked=True)	Stacked bars	Composition changes
area_stack	.plot.area(stacked=True)	Stacked area

Note: Jupyter Notebooks enable inline plotting (no pop-up) with %matplotlib inline, which is an IPython magic command that tells Jupyter Notebook to render Matplotlib plots directly inside the notebook output cell instead of in a separate window. You don’t need %matplotlib inline in Jupyter environment with IPython >= 7.

Later, when you learn Matplotlib, you will see why these methods of plotting are a lot easier to use. Pandas visualization balances ease of use with control over the figure. A lot of the plot calls also accept additional arguments of their parent matplotlib.plt call.

# %pip install pandas numpy     ### ensure pandas and numpy are installed; uncomment when done
# %pip install matplotlib       ### ensure matplotlib is installed; uncomment when done

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt ### because this is still matplotlib
# %matplotlib inline            ### uncomment if using Jupyter Notebook
                                ### unless using Jupyter < 7
                                ### use plt.show() to display plots in other environments  

11.1. Loading Data

df1 and df2 are some .csv data files you can read in as dataframes. Always use

• head() (how the dataset looks like),

• describe() (descriptive statistics), and

• shape (get dimension information; or just evaluate the dataframe)

to get to know your dataset.

df1 = pd.read_csv('../../data/df1',index_col=0)
df2 = pd.read_csv('../../data/df2')

### what does the data look like?

df1.head()

	A	B	C	D
2000-01-01	1.339091	-0.163643	-0.646443	1.041233
2000-01-02	-0.774984	0.137034	-0.882716	-2.253382
2000-01-03	-0.921037	-0.482943	-0.417100	0.478638
2000-01-04	-1.738808	-0.072973	0.056517	0.015085
2000-01-05	-0.905980	1.778576	0.381918	0.291436

### descriptive statistics

df1.describe()

	A	B	C	D
count	1000.000000	1000.000000	1000.000000	1000.000000
mean	-0.017755	0.048072	-0.001723	0.002432
std	0.957223	1.004197	0.982384	1.066366
min	-3.693201	-2.719020	-2.987971	-3.182746
25%	-0.639101	-0.652530	-0.690831	-0.676107
50%	-0.017793	0.058035	-0.012805	-0.044868
75%	0.623478	0.696946	0.706496	0.721699
max	3.412236	3.199850	3.342484	2.879793

df2.head()

	a	b	c	d
0	0.039762	0.218517	0.103423	0.957904
1	0.937288	0.041567	0.899125	0.977680
2	0.780504	0.008948	0.557808	0.797510
3	0.672717	0.247870	0.264071	0.444358
4	0.053829	0.520124	0.552264	0.190008

df2.describe()

	a	b	c	d
count	10.000000	10.000000	10.000000	10.000000
mean	0.460880	0.352935	0.587008	0.631597
std	0.340793	0.301272	0.284332	0.258158
min	0.039762	0.008948	0.103423	0.190008
25%	0.212334	0.179302	0.427949	0.457694
50%	0.371366	0.240298	0.555036	0.584144
75%	0.753558	0.515799	0.873619	0.837267
max	0.937288	0.997075	0.907307	0.977680

11.2. Style Sheets

The style sheet, or themes, basically creates a set of style rules that your plots follow. The use of a stylesheet gives your plots a unified look and feel, making them more professional. You can even create your own stylesheet.

Matplotlib has style sheets (or themes) you can use to make your plots look a little nicer. Popular stylesheets include:

• bmh (Bayesian Methods for Hackers)

• fivethirtyeight (FiveThirtyEight is a news site)

• ggplot (R’s ggplot2 default theme)

• dark_background

The syntax for using stylesheets in matplotlib is:

plt.style.use(style_name)

To see all the stylesheets available, use:

plt.style.available

Note that we use plt.style, which means we are using matlibplot here.

Note: Pandas by default pulls colors from Matplotlib’s axes.prop_cycle, a Matplotlib rcParam (runtime configuration parameter), which is a color iterator (cycler) that cycles through a list of predefined colors. That’s why you may see different colors (by default, 3) when you plot multiple lines.

Before plt.style.use(), let’s draw this histogram:

df1['A'].hist()

<Axes: >

Call the stylesheet. Let’s use the ggplot theme:

plt.style.use('ggplot')

After applying plt.style.use(ggplot):

df1['A'].hist()

<Axes: >

Now try plt.style.use(bmh):

plt.style.use('bmh')
df1['A'].hist()

<Axes: >

fivethirtyeight

plt.style.use('fivethirtyeight')
df1['A'].hist()

<Axes: >

A dark background theme:

plt.style.use('dark_background')
df1['A'].hist()

<Axes: >

Let’s stick with the ggplot style for now.

plt.style.use('ggplot')
# plt.style.use('bmh')

11.3. Plot Types

Let’s call some of these 11 plot type methods (the key terms shown in the list above, e.g. ‘box’, ‘barh’, etc) to see how they work.

### remember the dataframe

df2.head()

	a	b	c	d
0	0.039762	0.218517	0.103423	0.957904
1	0.937288	0.041567	0.899125	0.977680
2	0.780504	0.008948	0.557808	0.797510
3	0.672717	0.247870	0.264071	0.444358
4	0.053829	0.520124	0.552264	0.190008

11.3.1. Area Plot

df2.plot.area(alpha=0.4)

<Axes: >

11.3.2. Bar Plots

Bar plots are one of the most common ways to compare categorical data visually. They represent quantities as rectangular bars whose length (or height) corresponds to the value being measured. In Python, these are typically created using Matplotlib or Seaborn.

Both bar() and barh() create bar charts — the difference is simply orientation. Stacked bar charts, on the other hand, how multiple subcategories contribute to a total within each main category.

Plot Type	Orientation	Purpose	Best For
Bar Plot	Vertical	Compare category values	Simple category comparisons
Barh Plot	Horizontal	Compare category values with long labels	Readability and ranking-type data
Stacked Bar	Either	Show part-to-whole relationships	Composition of totals across categories

11.3.2.1. Vertical Bar Plot

### bar plot

df2.plot.bar()

<Axes: >

11.3.2.2. Horizontal Bar Plot

### bar plot horizontal

df2.plot.barh()

<Axes: >

### bar plot stacked

df2.plot.bar(stacked=True)

<Axes: >

11.3.3. Histograms

df1['A'].plot.hist(bins=100)

<Axes: ylabel='Frequency'>

11.3.4. Line Plots

# df1.plot.line(x=df1.index, y='B',figsize=(12,3),lw=1)

df1.plot.line(y='B',figsize=(10,6),lw=5)

<Axes: >

11.3.5. Scatter Plots

df1.plot.scatter(x='A',y='B')

<Axes: xlabel='A', ylabel='B'>

11.4. Color Maps

• You can use c to color based off another column value.

• Use cmap to indicate colormap to use.

• For all the colormaps, check out: http://matplotlib.org/users/colormaps.html

### the color of each point corresponds to the values in column C
### try them out

# df1.plot.scatter(x=df1['A'], y=df1['B'], c=df1['C'], cmap='coolwarm')  ### need to use x='A' instead of x=df1['A']
df1.plot.scatter(x='A', y='B', c='C', cmap='coolwarm')       ### color value from 'C'; colormap
df1.plot.scatter(x='A', y='B', c='C', cmap='viridis')      ### another colormap

<Axes: xlabel='A', ylabel='B'>

11.5. Size

Or use s to indicate size based off another column. s parameter needs to be an array, not just the name of a column:

### try different sizes

# df1.plot.scatter(x='A', y='B', s=df1['C']*200)
df1.plot.scatter(x='A',y='B',s=df1['C']*10)

/Users/tcn85/workspace/dsm/.venv/lib/python3.13/site-packages/matplotlib/collections.py:999: RuntimeWarning: invalid value encountered in sqrt
  scale = np.sqrt(self._sizes) * dpi / 72.0 * self._factor

<Axes: xlabel='A', ylabel='B'>

11.5.1. BoxPlots

df2.plot.box() # Can also pass a by= argument for groupby

<Axes: >

11.5.2. Hexagonal Bin Plot

Useful for Bivariate Data, alternative to scatterplot:

np.random.seed(42)
df = pd.DataFrame(np.random.randn(1000, 2), columns=['a', 'b'])
df.plot.hexbin(x='a', y='b', gridsize=25, cmap='Oranges')

<Axes: xlabel='a', ylabel='b'>

11.5.3. Kernel Density Estimation (KDE) Plot

• A smooth version of histogram

%pip install scipy    ### ensure scipy is installed; uncomment when done

df2['a'].plot.kde()

Requirement already satisfied: scipy in /Users/tcn85/workspace/dsm/.venv/lib/python3.13/site-packages (1.16.2)
Requirement already satisfied: numpy<2.6,>=1.25.2 in /Users/tcn85/workspace/dsm/.venv/lib/python3.13/site-packages (from scipy) (2.3.3)
Note: you may need to restart the kernel to use updated packages.

<Axes: ylabel='Density'>

11.5.4. Density/KDE Plot

df2.plot.density()

<Axes: ylabel='Density'>

Using density plot as an example of how Pandas visualization differs from Matplotlib and Seaborn:

Library	Function	Notes
Seaborn	sns.kdeplot()	most common, easy to use
Pandas	.plot(kind="density")	convenient for quick plots
Matplotlib + SciPy	gaussian_kde()	manual control over details

The end.