# Back To Back Bar Plot In MatPlotLib

Want to learn more? I recommend these Python books: Python for Data Analysis, Python Data Science Handbook, and Introduction to Machine Learning with Python.

Note: Based on: Sebastian Raschka.

## Preliminaries

```%matplotlib inline
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
```

## Create dataframe

```raw_data = {'first_name': ['Jason', 'Molly', 'Tina', 'Jake', 'Amy'],
'pre_score': [4, 24, 31, 2, 3],
'mid_score': [25, 94, 57, 62, 70],
'post_score': [5, 43, 23, 23, 51]}
df = pd.DataFrame(raw_data, columns = ['first_name', 'pre_score', 'mid_score', 'post_score'])
df
```
first_name pre_score mid_score post_score
0 Jason 4 25 5
1 Molly 24 94 43
2 Tina 31 57 23
3 Jake 2 62 23
4 Amy 3 70 51

## Make plot

```# input data, specifically the second and
# third rows, skipping the first column
x1 = df.ix[1, 1:]
x2 = df.ix[2, 1:]

# Create the bar labels
bar_labels = ['Pre Score', 'Mid Score', 'Post Score']

# Create a figure
fig = plt.figure(figsize=(8,6))

# Set the y position
y_pos = np.arange(len(x1))
y_pos = [x for x in y_pos]
plt.yticks(y_pos, bar_labels, fontsize=10)

# Create a horizontal bar in the position y_pos
plt.barh(y_pos,
# using x1 data
x1,
# that is centered
align='center',
# with alpha 0.4
alpha=0.4,
# and color green
color='#263F13')

# Create a horizontal bar in the position y_pos
plt.barh(y_pos,
# using NEGATIVE x2 data
-x2,
# that is centered
align='center',
# with alpha 0.4
alpha=0.4,
# and color green
color='#77A61D')

# annotation and labels
plt.xlabel('Tina\'s Score: Light Green. Molly\'s Score: Dark Green')
t = plt.title('Comparison of Molly and Tina\'s Score')
plt.ylim([-1,len(x1)+0.1])
plt.xlim([-max(x2)-10, max(x1)+10])
plt.grid()

plt.show()
```