I am currently using the matplotlib.pyplot module to plot floating horizontal bar charts of pandas dataframe.
I would like to know how I can add an additional bar with the sum / count of bars over specific segments. In other words, from the code and plot below, I would like building a dataframe giving as output :
KP 0.0 to KP 0.1 : 1 bar
KP 0.1 to KP 0.5 : 2 bars
KP 0.5 to KP 0.55 : 3 bars
KP 0.55 to KP 0.7 : 2 bars
etc...
Regards,
import pandas as pd
import matplotlib.pyplot as plt
#Create the pandas dataframe
d = {'KP_from' : [0.12, 0.84, 0.5, 0.7], 'KP_to' : [0.55, 0.05, 0.8, 0.75]}
df = pd.DataFrame(data = d)
#Create relavant variables for the floating hbar plot
start = df[['KP_from','KP_to']].min(axis = 1)
mid = df[['KP_from','KP_to']].mean(axis = 1)
width = abs(df.KP_from - df.KP_to)
yval = range(df.shape[0])
df['Direction'] = ['->' if df.KP_from.iloc[i] < df.KP_to.iloc[i] else '<-' for i in yval]
#Create the mpl figure : floating hbar plot
plt.figure()
plt.xlabel('KP')
plt.barh(y = yval, left = start, width = width)
#Add direction arrows at the center of the floating hbar
for i in yval:
plt.annotate(df.Direction.iloc[i], xy = (mid[i], yval[i]), va = 'center', ha = 'center')
plt.show()
Here is another approach. As others have already pointed out, this is first of all an intersection problem which can be solved without the bar plot.
def is_present(i_start, i_end, start, end):
return (
((start <= i_start) & (end > i_start)) |
((start > i_start) & (end <= i_end))
)
# Create the pandas dataframe
d = {'KP_from': [0.12, 0.84, 0.5, 0.7], 'KP_to': [0.55, 0.05, 0.8, 0.75]}
intervals = sorted(set(d['KP_from'] + d['KP_to']))
n_bins = [
sum([is_present(i, j, s, e) for s, e in zip(d['KP_from'], d['KP_to'])])
for i, j in zip(intervals, intervals[1:])
]
for i, j, c in zip(intervals, intervals[1:], n_bins):
print(f'KP {i} to KP {j} \t: {c} bars')
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