I am looking at some world ecological footprint data and I want to make a stacked bar chart of each type of footprint where the values stacked on top of each other are the same, but for different countries. So I started using 2 of the footprints just to get something working.
This is what I got to work (sort of):
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
# Create DataFrame from CSV file
df = pd.read_csv('countries.csv')
# Slice Series out of DF
cropFoot = df['Cropland Footprint']
grazeFoot = df['Grazing Footprint']
# Convert Series to list
cropFoot = list(cropFoot)
grazeFoot = list(grazeFoot)
X = range(163) # the lists have 163 entries
plt.bar(X, height=cropFoot)
plt.bar(X, height=grazeFoot, bottom = cropFoot)
plt.show()
Which generates the following plot:
I have 5 separate footprints I want to display on the x-axis so that the footprint data for each country is stacked on top of one another. Essentially, right now the x-axis is showing all 163 countries with the 2 footprints stacked. I want the opposite. So I want 5 x bars with 163 countries stacked on each bar.
Something like this (but with 163 pieces stacked, not 7):
Unsurprisingly, just swapping the X and height... doesn't work. And the result doesn't make any sense at all:
plt.bar(cropFoot, height=X)
plt.bar(grazeFoot, height=X, bottom = cropFoot)
plt.show()
As it looks like this:
Any advice on how to reverse this properly? This is the dataset I'm using, sourced from Kaggle.
Since you are already using a dataframe, you might want to try the bar plot method provided, which is a little easier to work with. To stack, just need to set the parameter stacked=True
. However, what gets stacked is the column names, so you'll have to transpose your dataframe first. It might look something like this:
footprints = ['Cropland Footprint', 'Grazing Footprint', ...] # fill with other footprints
data = df[footprints].T
data.plot.bar(stacked=True, legend=False) # you probably don't want a legend with 163 countries
As an example:
df = pd.DataFrame(
np.arange(200).reshape(40, 5),
index=[f'i{x}' for x in range(40)],
columns=[f'c{x}' for x in range(5)]
)
df.T.plot.bar(stacked=True, legend=False)