How can I fix my error.
Computer Networking: A Top-Down Approach (7th Edition)
7th Edition
ISBN:9780133594140
Author:James Kurose, Keith Ross
Publisher:James Kurose, Keith Ross
Chapter1: Computer Networks And The Internet
Section: Chapter Questions
Problem R1RQ: What is the difference between a host and an end system? List several different types of end...
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Question
How can I fix my error.
![```python
import matplotlib.pyplot as plt
xf = df_fer[['TPC-MEOH']]
yf = df_fer[['TEAC-MEOH']]
xnf = df_nf[['TPC-MEOH']]
ynf = df_nf[['TEAC-MEOH']]
# Answer to Exercise 4 here
import numpy as np # 'np' is the prefix that will identify numpy packages
from source.ellipses import confidence_ellipse # for representing the correlation
xfa = np.array(xf).flatten()
yfa = np.array(yf).flatten()
xnfa = np.array(xnf).flatten()
ynfa = np.array(ynf).flatten()
ax.scatter(xfa, yfa, s=6, color='blue')
confidence_ellipse(xfa, yfa, ax, label='FR', edgecolor='blue', linestyle='--')
ax.scatter(xnfa, ynfa, s=6, color='orange')
confidence_ellipse(xnfa, ynfa, ax, label='NF', edgecolor='orange', linestyle='--')
ax.set_title('Title', fontsize=16, fontweight='bold')
ax.set_xlabel('x1', fontweight='bold', fontsize=16)
ax.set_ylabel('y1', fontweight='bold', fontsize=16)
ax.legend(prop={"size":14})
f_tpc_m = df_fer[['TPC-MEOH']]
f_teac_m = df_fer[['TEAC-MEOH']]
f_frap_m = df_fer[['FRAP-MEOH']]
f_tpc_h = df_fer[['TPC-H2O']]
f_teac_h = df_fer[['TEAC-H2O']]
f_frap_h = df_fer[['FRAP-H2O']]
nf_tpc_m = df_nf[['TPC-MEOH']]
nf_teac_m = df_nf[['TEAC-MEOH']]
nf_frap_m = df_nf[['FRAP-MEOH']]
nf_tpc_h = df_nf[['TPC-H2O']]
nf_teac_h = df_nf[['TEAC-H2O']]
nf_frap_h = df_nf[['FRAP-H2O']]
```
### Explanation of Code
The provided code is structured to visualize and analyze data correlations using Matplotlib and a custom package for confidence ellipses. Here's a breakdown of its functionality:
- **Data Setup**: The code begins by importing necessary libraries—`](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F892e817a-9b32-4eeb-b8fc-5dd7ffde6479%2F7e644641-b59e-4872-b2ed-82bef031098f%2Fd81bcj7_processed.png&w=3840&q=75)
Transcribed Image Text:```python
import matplotlib.pyplot as plt
xf = df_fer[['TPC-MEOH']]
yf = df_fer[['TEAC-MEOH']]
xnf = df_nf[['TPC-MEOH']]
ynf = df_nf[['TEAC-MEOH']]
# Answer to Exercise 4 here
import numpy as np # 'np' is the prefix that will identify numpy packages
from source.ellipses import confidence_ellipse # for representing the correlation
xfa = np.array(xf).flatten()
yfa = np.array(yf).flatten()
xnfa = np.array(xnf).flatten()
ynfa = np.array(ynf).flatten()
ax.scatter(xfa, yfa, s=6, color='blue')
confidence_ellipse(xfa, yfa, ax, label='FR', edgecolor='blue', linestyle='--')
ax.scatter(xnfa, ynfa, s=6, color='orange')
confidence_ellipse(xnfa, ynfa, ax, label='NF', edgecolor='orange', linestyle='--')
ax.set_title('Title', fontsize=16, fontweight='bold')
ax.set_xlabel('x1', fontweight='bold', fontsize=16)
ax.set_ylabel('y1', fontweight='bold', fontsize=16)
ax.legend(prop={"size":14})
f_tpc_m = df_fer[['TPC-MEOH']]
f_teac_m = df_fer[['TEAC-MEOH']]
f_frap_m = df_fer[['FRAP-MEOH']]
f_tpc_h = df_fer[['TPC-H2O']]
f_teac_h = df_fer[['TEAC-H2O']]
f_frap_h = df_fer[['FRAP-H2O']]
nf_tpc_m = df_nf[['TPC-MEOH']]
nf_teac_m = df_nf[['TEAC-MEOH']]
nf_frap_m = df_nf[['FRAP-MEOH']]
nf_tpc_h = df_nf[['TPC-H2O']]
nf_teac_h = df_nf[['TEAC-H2O']]
nf_frap_h = df_nf[['FRAP-H2O']]
```
### Explanation of Code
The provided code is structured to visualize and analyze data correlations using Matplotlib and a custom package for confidence ellipses. Here's a breakdown of its functionality:
- **Data Setup**: The code begins by importing necessary libraries—`
![### Python Code Snippet Explanation
This image shows a Python code snippet used for data visualization with errors. Let's break it down into sections:
#### Data Preparation
- Various datasets are being prepared by filtering columns from a DataFrame `df_ferr` and `df_nfrl`:
- `f_tpc_m`, `f_teac_m`, `f_frap_m`: Extract columns `"TPC-MEOH"`, `"TEAC-MEOH"`, and `"FRAP-MEOH"`.
- `f_tpc_h`, `f_teac_h`, `f_frap_h`: Extract columns `"TPC-H2O"`, `"TEAC-H2O"`, and `"FRAP-H2O"`.
- `nf_tpc_m`, `nf_teac_m`, `nf_frap_m`: Extract columns `nf_"TPC-MEOH"`, `nf_"TEAC-MEOH"`, and `nf_"FRAP-MEOH"`.
- `nf_tpc_h`, `nf_teac_h`, `nf_frap_h`: Extract columns `nf_"TPC-H2O"`, `nf_"TEAC-H2O"`, and `nf_"FRAP-H2O"`.
#### Data Visualization
- A matplotlib figure and axes are created with dimensions 24x24.
- The `draw_confidence_ellipse` function is used to plot confidence ellipses comparing different data sets:
- Comparisons are made primarily between "TPC", "TEAC", and "FRAP" metrics between `"MEOH"` and `"H2O"` mediums.
- Each subplot (ax[0,0], ax[0,1], etc.) appears to visually capture these metrics.
#### Error and Debugging
- An `AttributeError` occurs: `'numpy.ndarray' object has no attribute 'scatter'`.
- This error is caused by trying to call `.scatter` on a `numpy.ndarray` object. The function intended for this purpose is not correctly used here.
### Summary of Graphs/Diagrams
The goal of this visualization is to create elliptical confidence intervals on various scatter plots to analyze the relationship and spread of different metrics (TPC, TEAC, FRAP) in two chemical environments, "MEOH" and "H2O". However, due to the error, the visualization is incomplete and troubleshooting is required to fix the `scatter` method usage in the code.](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F892e817a-9b32-4eeb-b8fc-5dd7ffde6479%2F7e644641-b59e-4872-b2ed-82bef031098f%2F8pu9tue_processed.png&w=3840&q=75)
Transcribed Image Text:### Python Code Snippet Explanation
This image shows a Python code snippet used for data visualization with errors. Let's break it down into sections:
#### Data Preparation
- Various datasets are being prepared by filtering columns from a DataFrame `df_ferr` and `df_nfrl`:
- `f_tpc_m`, `f_teac_m`, `f_frap_m`: Extract columns `"TPC-MEOH"`, `"TEAC-MEOH"`, and `"FRAP-MEOH"`.
- `f_tpc_h`, `f_teac_h`, `f_frap_h`: Extract columns `"TPC-H2O"`, `"TEAC-H2O"`, and `"FRAP-H2O"`.
- `nf_tpc_m`, `nf_teac_m`, `nf_frap_m`: Extract columns `nf_"TPC-MEOH"`, `nf_"TEAC-MEOH"`, and `nf_"FRAP-MEOH"`.
- `nf_tpc_h`, `nf_teac_h`, `nf_frap_h`: Extract columns `nf_"TPC-H2O"`, `nf_"TEAC-H2O"`, and `nf_"FRAP-H2O"`.
#### Data Visualization
- A matplotlib figure and axes are created with dimensions 24x24.
- The `draw_confidence_ellipse` function is used to plot confidence ellipses comparing different data sets:
- Comparisons are made primarily between "TPC", "TEAC", and "FRAP" metrics between `"MEOH"` and `"H2O"` mediums.
- Each subplot (ax[0,0], ax[0,1], etc.) appears to visually capture these metrics.
#### Error and Debugging
- An `AttributeError` occurs: `'numpy.ndarray' object has no attribute 'scatter'`.
- This error is caused by trying to call `.scatter` on a `numpy.ndarray` object. The function intended for this purpose is not correctly used here.
### Summary of Graphs/Diagrams
The goal of this visualization is to create elliptical confidence intervals on various scatter plots to analyze the relationship and spread of different metrics (TPC, TEAC, FRAP) in two chemical environments, "MEOH" and "H2O". However, due to the error, the visualization is incomplete and troubleshooting is required to fix the `scatter` method usage in the code.
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