barplot

fivecentplots can plot bar charts using fcp.bar. At a minimum, the bar function requires the following keywords:

• df: a pandas DataFrame

• x: the name of the categorical data column

• y: the name of the DataFrame column containing the values for the data in column x

Setup

Imports

%load_ext autoreload
%autoreload 2
%matplotlib inline
import fivecentplots as fcp
import pandas as pd
import numpy as np
import os, sys, pdb
osjoin = os.path.join
db = pdb.set_trace

Sample data

Read some fake bar chart data

df = pd.read_csv(osjoin(os.path.dirname(fcp.__file__), 'tests', 'fake_data_bar.csv'))
df.head()

	Liquid	pH	Measurement	T [C]
0	Lemon juice	2.4	A	25
1	Orange juice	3.5	A	25
2	Battery acid	1.0	A	25
3	Bottled water	6.7	A	25
4	Coke	3.0	A	25

Set theme

Optionally set the design theme

#fcp.set_theme('gray')
#fcp.set_theme('white')

Other

SHOW = False

Bar plot types

Vertical

Consider a DataFrame that contains several sets of measured pH data for various liquids. First we plot only one set of measured data (note that we envoke the keyword to rotate the x tick labels so they are all readable):

fcp.bar(df, x='Liquid', y='pH', show=SHOW, filter='Measurement=="A" & T [C]==25', tick_labels_major_x_rotation=90)

Horizontal

We can plot the same data horizontally by adding the keyword horizontal=True (notice now we do not need to rotate tick labels):

fcp.bar(df, x='Liquid', y='pH', show=SHOW, filter='Measurement=="A"', horizontal=True)

Error bars

Now we can include all measurement sets in a single bar char. Now the bars represent the mean value of all data sets. We can also add error bars to indidcate the standard deviation between measurements:

fcp.bar(df, x='Liquid', y='pH', show=SHOW, tick_labels_major_x_rotation=90, error_bars=True)

Grouping

Like the plot function, bar plots can grouped in various ways to provide further insight intot the data set. This includes a legend, “stacking”, row and/or col subplot values, and wrap values.

Legend

With a legend, each measurement set can be explicitly displayed in subsequent bars:

fcp.bar(df, x='Liquid', y='pH', show=SHOW, tick_labels_major_x_rotation=90, legend='Measurement')

Stacked

Alternatively, we can stack the measurements to give a cumulative bar using the keyword stacked=True (which makes absolutely no sense for this data set but whatever):

fcp.bar(df, x='Liquid', y='pH', show=SHOW, tick_labels_major_x_rotation=90, stacked=True, legend='Measurement')

Row/column plot

We can separate different conditions using row/col plots as shown below:

fcp.bar(df, x='Liquid', y='pH', show=SHOW, tick_labels_major_x_rotation=90, col='Measurement', row='T [C]', ax_hlines=0, ax_size=[300, 300])

Wrap plot

Alternatively, we can use a wrap plot to visualize:

fcp.bar(df, x='Liquid', y='pH', show=SHOW, tick_labels_major_x_rotation=90, wrap='Measurement', ax_size=[300, 300])