quartz/content/AI&DATA/Data Science/Data visualization with Matplotlib.md

https://kolibril13.github.io/plywood-gallery-matplotlib-examples/

https://matplotlib.org/stable/plot_types/basic/index.html

# Introduction to Data Visualization with Matplotlib

1. **Pyplot Interface Introduction**:
   ```python
   import matplotlib.pyplot as plt
   plt.plot([1, 2, 3, 4])
   plt.ylabel('some numbers')
   plt.show()
   ```

2. **Adding Data to Axes**:
   ```python
   plt.plot(['Jan', 'Feb', 'Mar', 'Apr'], [40, 42, 50, 60])
   plt.ylabel('Average Temp')
   plt.show()
   ```

3. **Combining Multiple Data Sets**:
   ```python
   plt.plot(['Jan', 'Feb', 'Mar', 'Apr'], [40, 42, 50, 60], label='Seattle')
   plt.plot(['Jan', 'Feb', 'Mar', 'Apr'], [30, 35, 40, 50], label='New York')
   plt.ylabel('Temp')
   plt.legend()
   plt.show()
   ```

4. **Customizing Plots**:
   ```python
   plt.plot(['Jan', 'Feb', 'Mar', 'Apr'], [40, 42, 50, 60], linestyle='--', color='r', marker='o')
   plt.ylabel('Temp')
   plt.show()
   ```

5. **Axes Labels and Titles**:
   ```python
   plt.plot(['Jan', 'Feb', 'Mar', 'Apr'], [40, 42, 50, 60])
   plt.xlabel('Month')
   plt.ylabel('Temp')
   plt.title('Monthly Temp in Seattle')
   plt.show()
   ```

6. **Creating Subplots**:
   ```python
   fig, ax = plt.subplots()
   ax.plot(['Jan', 'Feb', 'Mar', 'Apr'], [40, 42, 50, 60])
   plt.show()
   ```

7. **Subplots Customization**:
   ```python
   fig, (ax1, ax2) = plt.subplots(1, 2)
   ax1.plot(['Jan', 'Feb', 'Mar', 'Apr'], [40, 42, 50, 60])
   ax2.plot(['Jan', 'Feb', 'Mar', 'Apr'], [30, 35, 40, 50])
   plt.show()
   ```

# Time-series Data with Matplotlib

#timeseries

To create a DataFrame for `climate_change`, you can follow a structure similar to this:

```python
import pandas as pd

# Sample data structure
data = {
    "date": ["2020-01-01", "2020-01-02", "2020-01-03", ...],
    "co2": [414.7, 415.0, 415.3, ...],
    "relative_temp": [0.25, 0.27, 0.29, ...]
}

climate_change = pd.DataFrame(data)
climate_change['date'] = pd.to_datetime(climate_change['date'])
climate_change.set_index('date', inplace=True)
```

Replace the ellipses (`...`) with your actual data. This code sets up a DataFrame with date, CO2, and temperature data, converting the date column to a datetime format and setting it as the index, which is typical for time-series data.

1. **Plotting Basic Time-series Data**:
   ```python
   import matplotlib.pyplot as plt
   ax.plot(climate_change.index, climate_change['co2'])
   ax.set_xlabel('Time')
   ax.set_ylabel('CO2 (ppm)')
   plt.show()
   ```

2. **Zooming into Specific Time Frames**:
   - Decade Zoom:
     ```python
     sixties = climate_change["1960-01-01":"1969-12-31"]
     ax.plot(sixties.index, sixties['co2'])
     ```
   - Year Zoom:
     ```python
     sixty_nine = climate_change["1969-01-01":"1969-12-31"]
     ax.plot(sixty_nine.index, sixty_nine['co2'])
     ```

3. **Plotting Multiple Time-series Together**:
   ```python
   ax.plot(climate_change.index, climate_change["co2"])
   ax.plot(climate_change.index, climate_change["relative_temp"])
   ```

4. **Using Twin Axes for Different Variables**:
   ```python
   ax2 = ax.twinx()
   ax2.plot(climate_change.index, climate_change["relative_temp"])
   ```

5. **Differentiating Variables by Color**:
   ```python
   ax.plot(climate_change.index, climate_change["co2"], color='blue')
   ax2.plot(climate_change.index, climate_change["relative_temp"], color='red')
   ```

6. **Customizing Annotations on Time-series Plots**:
   - Basic Annotation:
     ```python
     ax2.annotate(">1 degree", xy=[pd.Timestamp("2015-10-06"), 1])
     ```
   - With Text Positioning and Arrows:
     ```python
     ax2.annotate(">1 degree", xy=(pd.Timestamp('2015-10-06'), 1), xytext=(pd.Timestamp('2008-10-06'), -0.2), arrowprops={})
     ```

7. **Creating a Function for Time-series Plotting**:
   ```python
   def plot_timeseries(axes, x, y, color, xlabel, ylabel):
       axes.plot(x, y, color=color)
       axes.set_xlabel(xlabel)
       axes.set_ylabel(ylabel, color=color)
   ```

Each section provides practical instructions and code snippets for effectively visualizing and analyzing time-series data using Matplotlib in Python.

# Quantitative Comparisons: Bar-Charts

1. **Olympic Medals Data**: Presents a dataset on Olympic medals and demonstrates basic bar chart plotting.
2. **Rotating Tick Labels**: Shows how to rotate axis labels for clarity.
3. **Visualizing Multiple Medal Types**: Explains stacking bars for different medal types (Gold, Silver, Bronze).
4. **Adding a Legend**: Illustrates how to add a legend to distinguish between different bars.
5. **Histograms**: Discusses creating histograms to represent distribution of data.
6. **Error Bars in Bar Charts**: Teaches how to add error bars to bar charts for statistical representation.
7. **Boxplots**: Describes creating boxplots for data distribution analysis.
8. **Scatter Plots**: Introduces scatter plots for comparing two quantitative variables.

Here are example codes for each key point from "Quantitative Comparisons: Bar-Charts":

1. **Olympic Medals Data**:
   ```python
   medals.plot(kind='bar')
   ```

2. **Rotating Tick Labels**:
   ```python
   plt.xticks(rotation=45)
   ```

3. **Visualizing Multiple Medal Types**:
   ```python
   medals.plot(kind='bar', stacked=True)
   ```

4. **Adding a Legend**:
   ```python
   plt.legend()
   ```

5. **Histograms**:
   ```python
   plt.hist(data)
   ```

6. **Error Bars in Bar Charts**:
   ```python
   plt.bar(x, height, yerr=error)
   ```

7. **Boxplots**:
   ```python
   data.boxplot()
   ```

8. **Scatter Plots**:
   ```python
   plt.scatter(x, y)
   ```

These examples provide a basic structure for each type of plot, which you can adapt and expand upon based on your specific data and visualization needs.