Update PDF links and add section on preparing

figures for sharing

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

@@ -4,7 +4,7 @@ https://matplotlib.org/stable/plot_types/basic/index.html
 
 # Introduction to Data Visualization with Matplotlib
 
-[pdf](https://github.com/ErdemOzgen/brain/blob/v4/docs/pdfs/matplot1.pdf)
+[See PDF link](https://github.com/ErdemOzgen/brain/blob/v4/docs/pdfs/matplot1.pdf)
 
 1. **Pyplot Interface Introduction**:
    ```python
@@ -64,7 +64,7 @@ https://matplotlib.org/stable/plot_types/basic/index.html
 # Time-series Data with Matplotlib
 
 #timeseries
-[pdf](https://github.com/ErdemOzgen/brain/blob/v4/docs/pdfs/matplot2.pdf)
+[See PDF link](https://github.com/ErdemOzgen/brain/blob/v4/docs/pdfs/matplot2.pdf)
 
 
 To create a DataFrame for `climate_change`, you can follow a structure similar to this:
@@ -147,7 +147,8 @@ Each section provides practical instructions and code snippets for effectively v
 
 # Quantitative Comparisons: Bar-Charts
 
-![[chapter3 (3).pdf]]
+[See PDF link](https://github.com/ErdemOzgen/brain/blob/v4/docs/pdfs/matplot3.pdf)
+
 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).
@@ -199,4 +200,56 @@ Here are example codes for each key point from "Quantitative Comparisons: Bar-Ch
    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.
+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.
+
+
+
+# Preparing Your Figures to Share with Others
+
+[See PDF link](https://github.com/ErdemOzgen/brain/blob/v4/docs/pdfs/matplot4.pdf)
+
+The document "Preparing Your Figures to Share with Others" discusses various aspects of refining and saving Matplotlib figures for presentation and sharing. Here are the key points with corresponding Python code examples:
+
+1. **Changing Plot Style**:
+   ```python
+   plt.style.use("ggplot")
+   ```
+
+2. **Choosing a Style**:
+   ```python
+   plt.style.use("seaborn-colorblind")
+   ```
+
+3. **Saving the Figure to File**:
+   ```python
+   fig.savefig("my_figure.png")
+   ```
+
+4. **Different File Formats**:
+   - Saving as JPEG:
+     ```python
+     fig.savefig("my_figure.jpg")
+     ```
+   - Saving as SVG:
+     ```python
+     fig.savefig("my_figure.svg")
+     ```
+
+5. **Resolution**:
+   ```python
+   fig.savefig("my_figure.png", dpi=300)
+   ```
+
+6. **Size**:
+   ```python
+   fig.set_size_inches([5, 3])
+   ```
+
+7. **Automating Figures from Data**:
+   ```python
+   for sport in sports:
+       sport_df = df[df["Sport"] == sport]
+       ax.bar(sport, sport_df["Height"].mean(), yerr=sport_df["Height"].std())
+   ```
+
+Each section provides guidelines and examples for effectively preparing and customizing figures in Matplotlib to enhance their communicative power and suitability for various sharing formats.

content/AI&DATA/Data Science/seaborn.md

@@ -1,5 +1,130 @@
 #seaborn
 
+# Introduction to Data Visualization with Seaborn
+
+
+[See PDF link](https://github.com/ErdemOzgen/brain/blob/v4/docs/pdfs/seaborn1.pdf)
+
+1. **Scatter Plot**: Demonstrates creating a scatter plot with height and weight data.
+   ```python
+   sns.scatterplot(x=height, y=weight)
+   plt.show()
+   ```
+
+2. **Count Plot**: Shows how to create a count plot using gender data.
+   ```python
+   sns.countplot(x=gender)
+   plt.show()
+   ```
+
+3. **DataFrames with Seaborn**: Explains using Pandas DataFrames with Seaborn for visualization.
+   ```python
+   df = pd.read_csv("masculinity.csv")
+   sns.countplot(x="how_masculine", data=df)
+   plt.show()
+   ```
+
+4. **Scatter Plot with 'hue'**: Teaches adding a third variable using 'hue' in scatter plots.
+   ```python
+   sns.scatterplot(x="total_bill", y="tip", data=tips, hue="smoker")
+   plt.show()
+   ```
+
+5. **Setting 'hue' Order and Colors**: Provides examples for customizing the order and colors of 'hue' in plots.
+   ```python
+   sns.scatterplot(x="total_bill", y="tip", data=tips, hue="smoker", hue_order=["Yes", "No"])
+   plt.show()
+   ```
+
+These examples help understand the versatility of Seaborn for different types of data visualizations, particularly when dealing with categorical data.
+
+# Introduction to Relational Plots and Subplots with Seaborn
+
+
+[See PDF link](https://github.com/ErdemOzgen/brain/blob/v4/docs/pdfs/seaborn2.pdf)
+
+1. **Relational Plots**: Explains the use of `relplot()` for creating relational plots like scatter and line plots. 
+   ```python
+   sns.relplot(x="total_bill", y="tip", data=tips, kind="scatter")
+   ```
+
+2. **Subplots**: Demonstrates creating subplots in rows, columns, or both, using `relplot()`.
+   ```python
+   sns.relplot(x="total_bill", y="tip", data=tips, kind="scatter", col="smoker")
+   ```
+
+3. **Customizing Scatter Plots**: Covers customizations like point size, style, and transparency.
+   ```python
+   sns.relplot(x="total_bill", y="tip", data=tips, kind="scatter", size="size", hue="size")
+   ```
+
+4. **Line Plots**: Discusses creating line plots and adding markers, turning off line style, and handling multiple observations per x-value.
+   ```python
+   sns.relplot(x="hour", y="NO_2_mean", data=air_df_mean, kind="line", style="location", hue="location")
+   ```
+
+These examples illustrate Seaborn's capabilities for creating complex plots and subplots, enhancing data visualization.
+
+# Count Plots and Bar Plots in Seaborn 
+
+
+[See PDF link](https://github.com/ErdemOzgen/brain/blob/v4/docs/pdfs/seaborn3.pdf)
+
+
+1. **Count Plot**: Demonstrates creating count plots for categorical data.
+   ```python
+   sns.countplot(x="category", data=df)
+   plt.show()
+   ```
+
+2. **Bar Plot**: Illustrates how to make bar plots to display the mean of quantitative variables for each category.
+   ```python
+   sns.barplot(x="category", y="value", data=df)
+   plt.show()
+   ```
+
+3. **Customizing Plots**: Discusses customizing plots by changing the order of categories, orientation, and adding confidence intervals.
+   ```python
+   sns.barplot(x="category", y="value", data=df, ci="sd")
+   plt.show()
+   ```
+
+These examples demonstrate Seaborn's capabilities in handling categorical data and the various ways to customize the visualization for clearer insights.
+
+
+# Changing Plot Style and Color in Seaborn
+
+
+[See PDF link](https://github.com/ErdemOzgen/brain/blob/v4/docs/pdfs/seaborn4.pdf)
+
+1. **Changing Plot Style**: Set different background styles.
+   ```python
+   sns.set_style("whitegrid")
+   ```
+
+2. **Customizing Color Palette**: Apply different color palettes.
+   ```python
+   sns.set_palette("husl")
+   ```
+
+3. **Setting Context**: Adjust the scale of plot elements and labels.
+   ```python
+   sns.set_context("talk")
+   ```
+
+4. **Adding Titles and Labels**: Enhance plot readability.
+   ```python
+   ax.set_title("Plot Title")
+   ax.set_xlabel("X Label")
+   ax.set_ylabel("Y Label")
+   ```
+
+These examples demonstrate how to effectively change the style, color, and context of plots in Seaborn, as well as how to add titles and labels for clarity.
+
+
+
+------------------------------------------------
+
 # Difference Between Catplot vs Relplot
 
 1. **Catplot**: The `catplot` function is used for plotting categorical data. It provides a high-level interface for drawing categorical plots onto a FacetGrid. This function provides access to several axes-level functions that show the relationship between a numerical and one or more categorical variables using one of several visual representations. Examples include box plots, violin plots, bar plots, and strip plots.
@@ -22,4 +147,174 @@ Here's a detailed explanation of how whiskers work in Seaborn's box plots:
 
 It's important to note that the default behavior for whiskers can be adjusted in Seaborn. You can change the multiplier for the IQR to adjust how far the whiskers extend from the box. Additionally, you can set whiskers to represent a specific percentile or a specific range of data, depending on your specific needs and the nature of your dataset.
 
-Using these whiskers, box plots provide a concise summary of the distribution of the data, highlighting the median, quartiles, and potential outliers. This makes them extremely useful for comparing distributions between several groups or datasets.
+Using these whiskers, box plots provide a concise summary of the distribution of the data, highlighting the median, quartiles, and potential outliers. This makes them extremely useful for comparing distributions between several groups or datasets.
+
+
+
+
+# Seaborn Intermediate
+
+[See PDF link](https://github.com/ErdemOzgen/brain/blob/v4/docs/pdfs/seabornInt1.pdf)
+
+The document "Intermediate Data Visualization with Seaborn" provides a comprehensive introduction to Seaborn, a Python library for data visualization. It covers various plotting techniques and how Seaborn integrates with other Python libraries like Matplotlib and Pandas. Key points include:
+
+1. **Matplotlib Integration**: Example - Using Matplotlib for basic plots before enhancing them with Seaborn.
+   ```python
+   import matplotlib.pyplot as plt
+   import pandas as pd
+   df = pd.read_csv("wines.csv")
+   fig, ax = plt.subplots()
+   ax.hist(df['alcohol'])
+   ```
+
+2. **Pandas for Data Analysis**: Example - Basic plotting with Pandas.
+   ```python
+   import pandas as pd
+   df = pd.read_csv("wines.csv")
+   df['alcohol'].plot.hist()
+   ```
+
+3. **Seaborn for Complex Visualizations**: Example - Creating histograms with Seaborn's `histplot`.
+   ```python
+   import seaborn as sns
+   sns.histplot(df['alcohol'])
+   ```
+
+4. **Distribution Plots**: Example - Using Seaborn's `displot` for distribution plots.
+   ```python
+   import seaborn as sns
+   sns.displot(df['alcohol'], kind='kde')
+   ```
+
+5. **Enhanced Plot Types**: Example - Combining KDE, rug plot, and cumulative distribution functions.
+   ```python
+   sns.displot(df['alcohol'], kind='ecdf')
+   ```
+
+6. **Regression Plots**: Example - Creating scatter plots with regression lines using `regplot` and `lmplot`.
+   ```python
+   sns.regplot(data=df, x="alcohol", y="pH")
+   sns.lmplot(data=df, x="alcohol", y="quality", hue="type")
+   ```
+
+These examples demonstrate how Seaborn can be used to create and enhance various types of data visualizations.
+
+
+
+
+# Chapter 2: Intermediate Data Visualization with Seaborn
+
+[See PDF link](https://github.com/ErdemOzgen/brain/blob/v4/docs/pdfs/seabornInt2.pdf)
+
+1. **Setting Styles**: Changing the overall appearance of plots.
+   ```python
+   sns.set_style('whitegrid')
+   sns.displot(df['Tuition'])
+   ```
+
+2. **Removing Axes Spines**: Simplifying plots by removing spines.
+   ```python
+   sns.set_style('white')
+   sns.displot(df['Tuition'])
+   sns.despine(left=True)
+   ```
+
+3. **Defining Custom Colors**: Assigning specific colors to plots.
+   ```python
+   sns.set(color_codes=True)
+   sns.displot(df['Tuition'], color='g')
+   ```
+
+4. **Using Different Palettes**: Demonstrating the use of various color palettes.
+   ```python
+   palettes = ['deep', 'muted', 'pastel', 'bright', 'dark', 'colorblind']
+   for p in palettes:
+       sns.set_palette(p)
+       sns.displot(df['Tuition'])
+   ```
+
+5. **Customizing with Matplotlib**: Further customizations using Matplotlib.
+   ```python
+   fig, ax = plt.subplots()
+   sns.histplot(df['Tuition'], ax=ax)
+   ax.set(xlabel='Tuition 2013-14', ylabel='Distribution', xlim=(0, 50000), title='2013-14 Tuition and Fees Distribution')
+   ```
+
+These examples show how Seaborn can be used in conjunction with Matplotlib for more detailed plot customizations.
+
+
+# Chapter 3
+
+
+[See PDF link](https://github.com/ErdemOzgen/brain/blob/v4/docs/pdfs/seabornInt3.pdf)
+
+1. **Stripplot** - Plotting individual data points.
+   ```python
+   sns.stripplot(data=df, y="DRG Definition", x="Average Covered Charges", jitter=True)
+   ```
+
+2. **Swarmplot** - Similar to stripplot but points are adjusted to avoid overlap.
+   ```python
+   sns.swarmplot(data=df, y="DRG Definition", x="Average Covered Charges")
+   ```
+
+3. **Boxplot** - Showing distributions with respect to categories.
+   ```python
+   sns.boxplot(data=df, y="DRG Definition", x="Average Covered Charges")
+   ```
+
+4. **Violinplot** - Combining boxplot and kernel density estimation.
+   ```python
+   sns.violinplot(data=df, y="DRG Definition", x="Average Covered Charges")
+   ```
+
+5. **Boxenplot** - An enhanced boxplot for larger datasets.
+   ```python
+   sns.boxenplot(data=df, y="DRG Definition", x="Average Covered Charges")
+   ```
+
+6. **Barplot** - Visualizing data with bar charts.
+   ```python
+   sns.barplot(data=df, y="DRG Definition", x="Average Covered Charges", hue="Region")
+   ```
+
+7. **Pointplot** - Points are connected by a line, useful for time series data.
+   ```python
+   sns.pointplot(data=df, y="DRG Definition", x="Average Covered Charges", hue="Region")
+   ```
+
+8. **Countplot** - Shows the count of observations in each categorical bin.
+   ```python
+   sns.countplot(data=df, y="DRG_Code", hue="Region")
+   ```
+
+These examples demonstrate various ways to visualize categorical data using Seaborn.
+
+# FacetGrid, catplot, and lmplot in Seaborn
+
+
+[See PDF link](https://github.com/ErdemOzgen/brain/blob/v4/docs/pdfs/seabornInt4.pdf)
+
+
+1. **FacetGrid** - Creating grids of plots based on a category:
+   ```python
+   g = sns.FacetGrid(df, col='HIGHDEG')
+   g.map(sns.boxplot, 'Tuition', order=['1', '2', '3', '4'])
+   ```
+
+2. **catplot** - Simplified facet grid for categorical data:
+   ```python
+   sns.catplot(x="Tuition", data=df, col="HIGHDEG", kind="box")
+   ```
+
+3. **lmplot** - Plotting regression models on a FacetGrid:
+   ```python
+   sns.lmplot(data=df, x="Tuition", y="SAT_AVG_ALL", col="HIGHDEG", fit_reg=False)
+   ```
+
+4. **lmplot with Regression** - Adding regression lines to plots:
+   ```python
+   sns.lmplot(data=df, x="Tuition", y="SAT_AVG_ALL", col="HIGHDEG", row="REGION")
+   ```
+
+These examples illustrate how to use Seaborn's FacetGrid, catplot, and lmplot functions to create complex, multi-faceted visualizations.