how to create multiple plot from a panda Dataframe I want to plot multiple plots.
The data is stored in a pandas dataframe and each row should be a seperate plot.
Each row has an ID (ZRD_ID) which doenst matter and a date (TAG) and 24 values to be plotted.

import pandas as pd
import numpy as np

df = pd.read_csv('./Result_set_edited.csv')
df = df.drop("ZRD_ID", axis=1).drop("TAG", axis=1)

x = df.iloc[[0]]
print(df.head())


returns:

     W01     W02     W03    W04    W05  ...     W20    W21    W22    W23    W24
0  72616  156076  141025  72629  72631  ...       0      0      0      0      0
1  67114  171650  139920  67291  67292  ...  172924  93511  72445  72445  72445
2  66893  161919  134041  66913  66911  ...  166244  86672  67114  67120  67124
3  66603  171297  134227  66615  66631  ...  166078  86622  66871  66877  66879
4  66759  167198  133523  67126  67128  ...  163999  74525  66562  66568  66574


To start easier, since I am really new to this I thought of plotting the first line alone first.

since the columns are named 'W01', 'W02', ... , 'W24' I thought I coould use them as labels for the x-axis.
I just didn't find a way to do so, since its the header of the df I guess.
So i created a new array and tried to plot it with the first row of my Dataframe:

y = np.arange(0,24,1)
y.reshape(1,24)
print(y)
print(df.iloc[[0]].values)
plt.plot(y, x)
plt.show()


when trying to plot my values I get the following Error:


  ValueError: x and y must have same first dimension, but have shapes
  (24,) and (1, 24)


thanksfor the help on how to fix the Error for plotting the first line.

PS: I would appreciate some hints on how to improve my question, since it is my first one.

Cheers
If I correctly got what you meant: you want to plot the first row of all the columns?
I guess this could work.
x= range(0,24)
y = df.iloc[0,:].values
xticks = df.columns.tolist()
I'm assuming you want a line plot

plt.plot(x,y)
plt.xticks(xticks) # to change the x ticks of the graph. 
plt.show() 

The '0' signifies the first row and ':' for all the columns.