Understanding the Problem and Identifying the Issue

Introduction

The given problem involves plotting multiple graphs in a single diagram using Python’s matplotlib library. The code provided attempts to use a for loop to iterate over each row of a pandas DataFrame (df) and plot the corresponding values from another DataFrame (df1), but it results in an incorrect output.

The Incorrect Code

x = df1['mrwSmpVWi']
c = df['c']
a = df['a']
b = df['b']

y = (c / (1 + (a) * np.exp(-b*(x))))

for number in df.Seriennummer:
    plt.plot(x,y, linewidth = 4)
    plt.title("TEST")
    plt.xlabel('Wind in m/s')
    plt.ylabel('Leistung in kWh')
    plt.xlim(0,25)
    plt.ylim(0,1900)
    plt.show()

The Issue

The issue with the provided code is that it attempts to plot x and y values for each row of df, but it does not account for the fact that there may be multiple rows in df1 corresponding to a single row in df. This results in an incorrect calculation of y values, leading to the dots in the diagram.

The Correct Approach

To correctly plot multiple graphs in a single diagram, we need to iterate over each row of df, calculate the corresponding values for x and y using df1 and other DataFrames (c, a, b), and then plot these values.

Using Subplots to Plot Multiple Graphs

Introduction

One way to plot multiple graphs in a single diagram is by using subplots. We can create a figure with multiple subplots, each containing one of the graphs we want to display.

Creating a Figure with Multiple Subplots

import matplotlib.pyplot as plt

fig, ax = plt.subplots(ncols=1,nrows=len(df.Seriennummer))

for i in range(len(df.Seriennummer)):
    x = df1.loc['Seriennummer'==df.Seriennummer.iloc[i]]['mrwSmpVWi']
    y = (c.loc['Seriennummer'==df.Seriennummer.iloc[i]] / (1 + (a.loc['Seriennummer'==df.Seriennummer.iloc[i]]) * np.exp(-b.loc['Seriennummer'==df.Seriennummer.iloc[i]]*(x))))
    ax[i].plot(x,y, linewidth = 4)

plt.show()

Explanation

In this corrected code:

• We create a figure with multiple subplots using plt.subplots(ncols=1,nrows=len(df.Seriennummer)).
• We then iterate over each row of df and calculate the corresponding values for x and y using df1, other DataFrames (c, a, b), and their respective loc indexing.
• For each subplot, we plot the calculated x and y values using ax[i].plot(x,y, linewidth = 4).
• Finally, we display all subplots using plt.show().

Using Matplotlib’s Built-in Subplotting Function

Introduction

Another way to plot multiple graphs in a single diagram is by using matplotlib’s built-in sub plotting function, subplots.

Creating a Figure with Multiple Subplots

import matplotlib.pyplot as plt
import numpy as np

fig, ax = plt.subplots(1,len(df.Seriennummer),sharey=True)

for i in range(len(df.Seriennummer)):
    x = df1.loc['Seriennummer'==df.Seriennummer.iloc[i]]['mrwSmpVWi']
    y = (c.loc['Seriennummer'==df.Seriennummer.iloc[i]] / (1 + (a.loc['Seriennummer'==df.Seriennummer.iloc[i]]) * np.exp(-b.loc['Seriennummer'==df.Seriennummer.iloc[i]]*(x))))
    ax[i].plot(x,y, linewidth = 4)

plt.show()

Explanation

In this code:

• We create a figure with multiple subplots using plt.subplots(1,len(df.Seriennummer),sharey=True).
• We then iterate over each row of df and calculate the corresponding values for x and y using df1, other DataFrames (c, a, b), and their respective loc indexing.
• For each subplot, we plot the calculated x and y values using ax[i].plot(x,y, linewidth = 4).
• Finally, we display all subplots using plt.show().

Using Seaborn’s JointPlot Function

Introduction

Seaborn is a Python visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics.

Creating a Figure with Multiple Graphs

import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np

sns.set()

fig, ax = sns.jointplot(x=df1['mrwSmpVWi'], y=(c / (1 + (a * np.exp(-b*x)))), kind='scatter')

plt.show()

Explanation

In this code:

• We import the necessary libraries.
• We set the seaborn style using sns.set().
• We create a joint plot of x and y values using sns.jointplot(x=df1['mrwSmpVWi'], y=(c / (1 + (a * np.exp(-b*x)))), kind='scatter').
• Finally, we display the plot using plt.show().

Conclusion

Plotting multiple graphs in a single diagram can be achieved through various methods, including using subplots, seaborn’s joint plot function, and other visualization libraries. By choosing the appropriate method based on your data and desired output, you can effectively communicate complex information to your audience.

Last modified on 2024-06-12