column shows the 3 methanol-based pairs. Each plot will look like your plot for Exercise 1 above: it should includ ellipses for fermented and unfermented. Each subplot should have its own axis labels and title. In order to keep your code from exploding, you should create a function whose inputs are two columns of data, v two features. You will then call this function 6 times to produce the 6 subplots. import matplotlib.pyplot as plt xf= df_fer[['TPC-MECH']] |yf= df_fer[['TEAC-MEOH¹]] xnf = df_nf[['TPC-MEOH']] ynf = df_nf[['TEAC-MEOH']]

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How do you fix this error. Or is there another way to plot six confidence ellipse in 3x2 grid of plots using subplot (IN PYTHON)

The text describes a visualization task using Python's Matplotlib to create a 3x2 grid of plots, organizing data into two main columns comparing water-based and methanol-based pairs. Each subplot will display scattered points and confidence ellipses to differentiate fermented and unfermented samples with distinct axis labels and titles. Here's the transcription and explanation:

### Code Snippet Explanation

#### Initial Setup
- Import required libraries:
  ```python
  import matplotlib.pyplot as plt
  import numpy as np
  from source.ellipses import confidence_ellipse
  ```

- Load data for plotting:
  ```python
  xf = df_fer[['TPC-MEOH']]
  yf = df_fer[['TEAC-MEOH']]
  xfna = df_nf[['TPC-MEOH']]
  ynfna = df_nf[['TEAC-MEOH']]
  ```

#### Plotting Function
1. **Prepare Data:**
   - Flatten arrays for plotting:
     ```python
     xfa = np.array(xf).flatten()
     yfa = np.array(yf).flatten()
     xfna = np.array(xfna).flatten()
     ynfna = np.array(ynfna).flatten()
     ```

2. **Create Subplots:**
   - Initialize subplots:
     ```python
     ax = plt.subplots()
     ```

3. **Plot Data Points and Confidence Ellipses:**
   - Plot fermented data:
     ```python
     plt.scatter(xfa, yfa, s=6, color='blue')
     confidence_ellipse(xfa, yfa, ax, n_std=1, label='F', edgecolor='blue', linestyle='--')
     ```
   - Plot unfermented data:
     ```python
     plt.scatter(xfna, ynfna, s=6, color='orange')
     confidence_ellipse(xfna, ynfna, ax, n_std=1, label='NF', edgecolor='orange', linestyle='--')
     ```

4. **Format Plot:**
   - Set the title and labels:
     ```python
     ax.set_title('Title', fontsize=16, fontweight='bold')
     ax.set_xlabel('x1', fontweight='bold', fontsize=16)
     ax.set_ylabel('y1', fontweight='bold', fontsize=16)
     ```
   - Legend and label size:
     ```python
Transcribed Image Text:The text describes a visualization task using Python's Matplotlib to create a 3x2 grid of plots, organizing data into two main columns comparing water-based and methanol-based pairs. Each subplot will display scattered points and confidence ellipses to differentiate fermented and unfermented samples with distinct axis labels and titles. Here's the transcription and explanation: ### Code Snippet Explanation #### Initial Setup - Import required libraries: ```python import matplotlib.pyplot as plt import numpy as np from source.ellipses import confidence_ellipse ``` - Load data for plotting: ```python xf = df_fer[['TPC-MEOH']] yf = df_fer[['TEAC-MEOH']] xfna = df_nf[['TPC-MEOH']] ynfna = df_nf[['TEAC-MEOH']] ``` #### Plotting Function 1. **Prepare Data:** - Flatten arrays for plotting: ```python xfa = np.array(xf).flatten() yfa = np.array(yf).flatten() xfna = np.array(xfna).flatten() ynfna = np.array(ynfna).flatten() ``` 2. **Create Subplots:** - Initialize subplots: ```python ax = plt.subplots() ``` 3. **Plot Data Points and Confidence Ellipses:** - Plot fermented data: ```python plt.scatter(xfa, yfa, s=6, color='blue') confidence_ellipse(xfa, yfa, ax, n_std=1, label='F', edgecolor='blue', linestyle='--') ``` - Plot unfermented data: ```python plt.scatter(xfna, ynfna, s=6, color='orange') confidence_ellipse(xfna, ynfna, ax, n_std=1, label='NF', edgecolor='orange', linestyle='--') ``` 4. **Format Plot:** - Set the title and labels: ```python ax.set_title('Title', fontsize=16, fontweight='bold') ax.set_xlabel('x1', fontweight='bold', fontsize=16) ax.set_ylabel('y1', fontweight='bold', fontsize=16) ``` - Legend and label size: ```python
### Code Overview

The image displays a Python script utilizing libraries such as `pandas` and `matplotlib` for data manipulation and visualization.

#### Code Explanation:

1. **Data Selection:**
   - Data is extracted from a DataFrame `df_ferl`, with specific slices for 'TPC-MEOH', 'TEAC-MEOH', 'FRAP-MEOH', and similarly for another DataFrame `df_nf` with 'TPC-H2O', 'TEAC-H2O', 'FRAP-H2O'.

2. **Data Mapping:**
   - Specific columns are selected from the DataFrames to separate the TPC, TEAC, and FRAP values for 'ME-OH' and 'H2O' treatments.

3. **Plotting:**
   - A figure with a size of `(24, 24)` is created, and six subplots are defined in a 2x3 grid.
   - The function `draw_confidence_ellipse` is used to plot confidence ellipses for various data selections, juxtaposing 'MEOH' and 'H2O' treatments for TPC, TEAC, and FRAP.

4. **Scatter Plots:**
   - Blue and orange scatter plots are created with labels 'FR' and 'NF', indicating different datasets.

5. **Error Handling:**
   - An `AttributeError` is raised, indicating that a `tuple` object lacks the attribute `transData`. This error occurs on line 58 of the code in the `confidence_ellipse` function, specifically when attempting to call `ax.transData`.

### Error Explanation

- **AttributeError:** 
  - This error suggests that the code is trying to use an attribute or method that is not available for the specified object. The problem occurs in the `confidence_ellipse` function when trying to reference `transData` from `ax`, which should be replaced by `ax.transData`.

### Diagram Description

There is no diagram visible in the code snippet itself, but the goal is to visualize statistical data distributions using ellipses on `matplotlib` plots, thus identifying relationships or variances between different treatments (MEOH, H2O) in TPC, TEAC, and FRAP indices.

### Conclusion:

The script aims to visualize and compare biochemical data sets by plotting confidence ellipses on scatter plots. Addressing the `AttributeError` by ensuring correct attribute methods should
Transcribed Image Text:### Code Overview The image displays a Python script utilizing libraries such as `pandas` and `matplotlib` for data manipulation and visualization. #### Code Explanation: 1. **Data Selection:** - Data is extracted from a DataFrame `df_ferl`, with specific slices for 'TPC-MEOH', 'TEAC-MEOH', 'FRAP-MEOH', and similarly for another DataFrame `df_nf` with 'TPC-H2O', 'TEAC-H2O', 'FRAP-H2O'. 2. **Data Mapping:** - Specific columns are selected from the DataFrames to separate the TPC, TEAC, and FRAP values for 'ME-OH' and 'H2O' treatments. 3. **Plotting:** - A figure with a size of `(24, 24)` is created, and six subplots are defined in a 2x3 grid. - The function `draw_confidence_ellipse` is used to plot confidence ellipses for various data selections, juxtaposing 'MEOH' and 'H2O' treatments for TPC, TEAC, and FRAP. 4. **Scatter Plots:** - Blue and orange scatter plots are created with labels 'FR' and 'NF', indicating different datasets. 5. **Error Handling:** - An `AttributeError` is raised, indicating that a `tuple` object lacks the attribute `transData`. This error occurs on line 58 of the code in the `confidence_ellipse` function, specifically when attempting to call `ax.transData`. ### Error Explanation - **AttributeError:** - This error suggests that the code is trying to use an attribute or method that is not available for the specified object. The problem occurs in the `confidence_ellipse` function when trying to reference `transData` from `ax`, which should be replaced by `ax.transData`. ### Diagram Description There is no diagram visible in the code snippet itself, but the goal is to visualize statistical data distributions using ellipses on `matplotlib` plots, thus identifying relationships or variances between different treatments (MEOH, H2O) in TPC, TEAC, and FRAP indices. ### Conclusion: The script aims to visualize and compare biochemical data sets by plotting confidence ellipses on scatter plots. Addressing the `AttributeError` by ensuring correct attribute methods should
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