Change thickness of lines based on a condition-CodePudding

I've got this following graph

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

CodePudding user response：

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))