Change thickness of lines based on a condition

March 9, 2022

I’ve got this following graph 1 obtained from this piece of code[2]

import pandas as pd
import matplotlib.ticker as ticker
import matplotlib.pyplot as plt
dfMonday = pd.read_csv( "0.Mon.csv", encoding = "ISO-8859-1", sep = ';')

dfSlotMean = dfMonday.groupby('slotID', as_index=False).agg( NMonUn=('date', 'nunique'),NMonTot = ('date', 'count'), MeanBPM=('tempo', 'mean') )
#print(dfSlotMean)

dfSlotMean.drop(dfSlotMean[dfSlotMean.NMonUn< 3].index, inplace=True)
    
df = pd.DataFrame(dfSlotMean)

df.to_csv('1.silMonday.csv', sep = ';', index=False)

print(df)

tick_spacing = 1
fig, ax = plt.subplots(1, 1)
for _, r in df.iterrows():
    ax.plot([r['slotID'], r['slotID']+1], [r['MeanBPM']]*2)
    ax.xaxis.grid(True)
    ax.xaxis.set_major_locator(ticker.MultipleLocator(tick_spacing))

This is the output i’ve got:

    slotID  NMonUn  NMonTot     MeanBPM
0        7      11       78  129.700564
2       11       6       63  123.372397
3       12       6       33  120.625667
4       13       5       41  124.516341
5       14       4       43  118.904512
6       15       3       13  116.380538
7       16       3       42  119.670881
8       17       5       40  125.424125
9       18       6       45  130.540578
10      19       9       58  128.180172
11      20       5       44  125.596045

I would like to change the thickness of lines based on ‘NMonUn’ values in order to have thicker lines for higher values and vice versa

>Solution :

You can use the linewidth parameter and set it to the NMonUN value

tick_spacing = 1
fig, ax = plt.subplots(1, 1)
for _, r in df.iterrows():
    ax.plot([r['slotID'], r['slotID']+1], [r['MeanBPM']]*2, linewidth=r['NMonUN'])
    ax.xaxis.grid(True)
    ax.xaxis.set_major_locator(ticker.MultipleLocator(tick_spacing))