How to generate stacked BAR plot in Python?
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# How to generate stacked BAR plot in Python?

This recipe helps you generate stacked BAR plot in Python

## Recipe Objective

Graphical representation of data gives us a better idea of the data. Bar graph is one of the way to do that.

So this is the recipe on how we can generate stacked BAR plot in Python.

## Step 1 - Importing Library

``` import pandas as pd import matplotlib.pyplot as plt ```

We have only imported pandas and matplotlib which is needed.

## Step 2 - Creating a dataframe

We have created a dictionary with different values and passed it to pd.DataFrame to create a 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"]) print(df) ```

## Step 2 - Creating Bar Graph

We have created a bar graph for that we have to set various things. We have fixed bar width, bar boundries and their position. ``` f, ax = plt.subplots(1, figsize=(10,5)) bar_width = 1 bar_l = [i for i in range(len(df["pre_score"]))] tick_pos = [i+(bar_width/2)-0.5 for i in bar_l] ``` Here we have set the stack segment of the bar graph, by calculating total and individual sum and their percentage. We have also set the position of the bar. ``` totals = [i+j+k for i,j,k in zip(df["pre_score"], df["mid_score"], df["post_score"])] pre_rel = [i / j * 100 for i,j in zip(df["pre_score"], totals)] mid_rel = [i / j * 100 for i,j in zip(df["mid_score"], totals)] post_rel = [i / j * 100 for i,j in zip(df["post_score"], totals)] ax.bar(bar_l, pre_rel, label="Pre Score", alpha=0.9, width=bar_width, edgecolor="white") ax.bar(bar_l, mid_rel, bottom=pre_rel, label="Mid Score", alpha=0.9, width=bar_width, edgecolor="white") ax.bar(bar_l, post_rel, bottom=[i+j for i,j in zip(pre_rel, mid_rel)], label="Post Score", alpha=0.9, width=bar_width, edgecolor="white") ``` We are setting the label of x axis, y axis and other parameters of the plot. ``` print(tick_pos) plt.xticks(tick_pos, df["first_name"]) ax.set_ylabel("Percentage") ax.set_xlabel("") plt.xlim([min(tick_pos)-bar_width, max(tick_pos)+bar_width]) plt.ylim(-10, 110) plt.setp(plt.gca().get_xticklabels(), rotation=45) plt.show() ```

```  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
[0.0, 1.0, 2.0, 3.0, 4.0]
```

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