Nagendra18/Matplotlib-Seaborn

1import seaborn as sns2import numpy as np3import matplotlib.pyplot as plt4import streamlit as st5from matplotlib.sankey import Sankey6import scipy.stats as stats7import pySankey8from pySankey.sankey import sankey9 10 11 12st.title(":blue[Catplot]")13st.markdown("<hr style='border: none; height: 5px; background-color: purple;'>", unsafe_allow_html=True)14 15st.markdown("""16A **catplot** in Seaborn is a versatile function that creates a multi-plot grid for categorical data. It can visualize various relationships and distributions among categorical and continuous variables.17 18**Types of Analysis:**19- **Univariate:** Analyzing a single categorical variable.20- **Bivariate:** Analyzing the relationship between a categorical variable and a continuous variable.21- **Multivariate:** Analyzing multiple categorical and continuous variables.22""")23plt.title('Box Plot of Sepal Length by Species')24st.write("""25```python26iris = sns.load_dataset('iris')27 28sns.catplot(x='species', y='sepal_length', data=iris, kind='box')29st.pyplot(plt)30```31""")32iris = sns.load_dataset('iris')33 34sns.catplot(x='species', y='sepal_length', data=iris, kind='box')35st.pyplot(plt)36 37 38 39plt.title('Violin Plot of Age by Class')40st.write("""41```python42titanic = sns.load_dataset('titanic')43sns.catplot(x='class', y='age', data=titanic, kind='violin')44st.pyplot(plt)45```46""")47titanic = sns.load_dataset('titanic')48sns.catplot(x='class', y='age', data=titanic, kind='violin')49st.pyplot(plt)50 51 52 53st.title(":blue[Sankey Plot]")54st.markdown("<hr style='border: 2px dashed rainbow;'>", unsafe_allow_html=True)55 56st.markdown("""57A **Sankey plot** is a specialized flow diagram that visualizes the flow and relationships between entities. It is commonly used to show the movement or distribution of resources, such as energy, money, or information.58 59**Types of Analysis:**60- **Bivariate:** Analyzing the relationship between two categorical variables, showing how they contribute to overall flow.61- **Multivariate:** Representing multiple dimensions of relationships or flows.62""")63 64 65plt.title('Sankey Diagram Example')66st.write("""67```python68import pySankey69from pySankey.sankey import sankey70 71f1=np.random.choice(["apple","mango","banannaa","grap","kiwi"],size=(10,))72f2=np.random.choice(["apple","mango","banannaa","grap",],size=(10,))73cost=np.random.randint(10,50,size=(10,))74pd.DataFrame({"fruit_1":f1,"fruit_2":f2,"cost":v1})75sankey(left=f1,right=f2)76 77 78 79 80```81""")82 83 84st.image("https://cdn-uploads.huggingface.co/production/uploads/66be1362737c4ed890949fa1/JUNwr0Irpt_u-CAAmmfU2.png")85st.write("""86```python87import pySankey88from pySankey.sankey import sankey89 90f1=np.random.choice(["apple","mango","banannaa","grap","kiwi"],size=(10,))91f2=np.random.choice(["apple","mango","banannaa","grap",],size=(10,))92cost=np.random.randint(10,50,size=(10,))93pd.DataFrame({"fruit_1":f1,"fruit_2":f2,"cost":v1})94  95sankey(left=f1,right=f2,leftWeight=v1,rightWeight=v1)96```97""")98 99st.image("https://cdn-uploads.huggingface.co/production/uploads/66be1362737c4ed890949fa1/StNAU5eY8UKDGuW0BGAdU.png")100 101 102 103st.title(":blue[Q-Q Plot]")104st.markdown("<hr style='border: none; height: 5px; background-color: pink;'>", unsafe_allow_html=True)105 106st.markdown("""107A **Q-Q plot** (Quantile-Quantile plot) is a graphical tool used to compare the distribution of a dataset against a theoretical distribution (like normal distribution) or another dataset. It helps in assessing if the data follows a specific distribution.108 109**Types of Analysis:**110- **Univariate:** Analyzing the distribution of a single variable.111""")112 113st.title("Q-Q Plot Example")114st.subheader("Histogram of Generated Data")115st.write("""116```python117data = np.random.normal(loc=0, scale=1, size=1000)118 119 120 121plt.figure(figsize=(10, 5))122plt.hist(data, bins=30, alpha=0.7, color='blue', edgecolor='red')123plt.title("Histogram")124plt.xlabel("Value")125plt.ylabel("Frequency")126st.pyplot(plt)127```128""")129data = np.random.normal(loc=0, scale=1, size=1000)130 131 132 133plt.figure(figsize=(10, 5))134plt.hist(data, bins=30, alpha=0.7, color='blue', edgecolor='red')135plt.title("Histogram")136plt.xlabel("Value")137plt.ylabel("Frequency")138st.pyplot(plt)139 140 141 142st.subheader("Q-Q Plot")143st.write("""144```python145plt.figure(figsize=(10, 5))146stats.probplot(data, dist="norm", plot=plt)147plt.title("Q-Q Plot")148plt.xlabel("Theoretical Quantiles")149plt.ylabel("Sample Quantiles")150st.pyplot(plt)151```152""")153plt.figure(figsize=(10, 5))154stats.probplot(data, dist="norm", plot=plt)155plt.title("Q-Q Plot")156plt.xlabel("Theoretical Quantiles")157plt.ylabel("Sample Quantiles")158st.pyplot(plt)159