histogram of dataframe columns side-by-side-CodePudding

Suppose I have this df:

df = pd.DataFrame(
    {'id': [1, 2, 3, 4, 5],
 'actual': [412.6,741.24,1098.30,20025.87,1506.0],
 'calculated': [315.24,517.61,998.38,7438.03,1503.32]}
)

df

   id    actual    calculated
0   1    412.60      315.24
1   2    741.24      517.61
2   3   1098.30      998.38
3   4  20025.87     7438.03
4   5   1506.00     1503.32

And I want to plot a histogram of actual and calculated side-by-side for comparison. This code didn't work as intended.

fig, ax = plt.subplots()

a_heights, a_bins = np.histogram(df['actual'])
b_heights, b_bins = np.histogram(df['calculated'], bins=a_bins)

width = (a_bins[1] - a_bins[0])/3

ax.bar(a_bins[:-1], a_heights, width=width, facecolor='cornflowerblue')
ax.bar(b_bins[:-1] width, b_heights, width=width, facecolor='seagreen')

I want to show the lengths on y-axis (412.60/315.24; 741.24/517.61 etc)

CodePudding user response：

Use seaborn it's easier to manipulate side by side bar plots than matplotlib

import seaborn as sns
df_m = df.melt(id_vars='id')
sns.barplot(x='id', y='value', hue='variable', data=df_m)

Also for sake of completion giving an example of matplotlib side by side histograms.

import matplotlib.pyplot as plt 
x = np.random.normal(10,5,100)
y = np.random.normal(20,5,100)
plt.hist([x, y])