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¶

In [1]:

%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
st = pdb.set_trace

Sample data¶

Read some fake bar chart data

In [2]:

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

Out[2]:

	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

In [3]:

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

Other¶

In [4]:

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):

In [5]:

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):

In [6]:

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:

In [7]:

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:

In [8]:

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):

In [9]:

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:

In [10]:

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:

In [11]:

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