How to generate BAR plot using pandas DataFrame?

How to generate BAR plot using pandas DataFrame?

How to generate BAR plot using pandas DataFrame?

This recipe helps you generate BAR plot using pandas DataFrame

This python source code does the following: 1. Creates and converts data dictionary into dataframe 2. Groups different horizontal bar graphs for simultaneous comparison 3. Plots the bar graphs by adjusting the position of bars
In [2]:
## How to generate BAR plot using pandas DataFrame
def Snippet_113():
    print(format('How to use timeseries using pandas DataFrame','*^82'))
    import warnings

    # load libraries
    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',
    print(); print(df)

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

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

    # Create a figure

    # Set the y position
    y_pos = np.arange(len(Molly))
    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, Molly, align='center', alpha=0.4, color='#263F13')
    plt.barh(y_pos, -Tina, align='center', alpha=0.4, color='#77A61D')

    # annotation and labels
    plt.xlabel('Tina\'s Score: Light Green. Molly\'s Score: Dark Green')
    plt.title('Comparison of Molly and Tina\'s Score')
    plt.xlim([-max(Tina)-10, max(Tina)+10])

*******************How to use timeseries using pandas DataFrame*******************

  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

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