I have a notebook with 2* bar charts, one is winter data & one is summer data. I have counted the total of all the crimes and plotted them in a bar chart, using code:
ax = summer["crime_type"].value_counts().plot(kind='bar')
plt.show()
Which shows a graph like:
I have another chart nearly identical, but for winter:
ax = winter["crime_type"].value_counts().plot(kind='bar')
plt.show()
And I would like to have these 2 charts compared against one another in the same bar chart (Where every crime on the x axis has 2 bars coming from it, one winter & one summer).
I have tried, which is just me experimenting:
bx = (summer["crime_type"],winter["crime_type"]).value_counts().plot(kind='bar')
plt.show()
Any advice would be appreciated!
The following generates dummies of your data and does the grouped bar chart you wanted:
import random
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
s = "Crime Type Summer|Crime Type Winter".split("|")
# Generate dummy data into a dataframe
j = {x: [random.choice(["ASB", "Violence", "Theft", "Public Order", "Drugs"]
) for j in range(300)] for x in s}
df = pd.DataFrame(j)
index = np.arange(5)
bar_width = 0.35
fig, ax = plt.subplots()
summer = ax.bar(index, df["Crime Type Summer"].value_counts(), bar_width,
label="Summer")
winter = ax.bar(index+bar_width, df["Crime Type Winter"].value_counts(),
bar_width, label="Winter")
ax.set_xlabel('Category')
ax.set_ylabel('Incidence')
ax.set_title('Crime incidence by season, type')
ax.set_xticks(index + bar_width / 2)
ax.set_xticklabels(["ASB", "Violence", "Theft", "Public Order", "Drugs"])
ax.legend()
plt.show()
With this script I got:
You can check out the demo in the matplotlib docs here: https://matplotlib.org/gallery/statistics/barchart_demo.html
The important thing to note is the index!
index = np.arange(5) # Set an index of n crime types
...
summer = ax.bar(index, ...)
winter = ax.bar(index+bar_width, ...)
...
ax.set_xticks(index + bar_width / 2)
These are the lines that arrange the bars on the horizontal axis so that they are grouped together.
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