# 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

```# Create a list of the mean scores for each variable
mean_values = [df['pre_score'].mean(), df['mid_score'].mean(), df['post_score'].mean()]

# Create a list of variances, which are set at .25 above and below the score
variance = [df['pre_score'].mean() * 0.25, df['pre_score'].mean() * 0.25, df['pre_score'].mean() * 0.25]

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

# Create the x position of the bars
x_pos = list(range(len(bar_labels)))

# Create the plot bars
# In x position
plt.bar(x_pos,
# using the data from the mean_values
mean_values,
# with a y-error lines set at variance
yerr=variance,
# aligned in the center
align='center',
# with color
color='#FFC222',
# alpha 0.5
alpha=0.5)

# add a grid
plt.grid()

# set height of the y-axis
max_y = max(zip(mean_values, variance)) # returns a tuple, here: (3, 5)
plt.ylim([0, (max_y[0] + max_y[1]) * 1.1])

# set axes labels and title
plt.ylabel('Score')
plt.xticks(x_pos, bar_labels)
plt.title('Mean Scores For Each Test')

plt.show()
```