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# eda.py
import streamlit as st
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import phik # Ensure this library is installed
@st.cache_data
def load_and_preprocess_data():
df = pd.read_csv("weatherAUS.csv")
return df
def plot_data_distributions(df):
sns.set_style('whitegrid')
for column in df.columns:
plt.figure(figsize=(8,4))
if len(df[column].unique()) > 10:
sns.histplot(df[column], kde=True, color='skyblue')
plt.title(f'Distribution of {column}')
else:
sns.countplot(x=column, data=df, palette='Set2')
plt.title(f'Count of different classes in {column}')
st.pyplot(plt)
def calculate_phi_k_correlation(df):
phi_k_correlation = df.phik_matrix()
plt.figure(figsize=(12, 10))
sns.heatmap(phi_k_correlation, annot=True, fmt=".2f", linewidths=.5, cmap='coolwarm')
plt.title('Phi_k Correlation Matrix Heatmap')
st.pyplot(plt)
def perform_temporal_feature_extraction(df):
df['Date'] = pd.to_datetime(df['Date'])
df['Year'] = df['Date'].dt.year
df['Month'] = df['Date'].dt.month
return df
def plot_annual_and_monthly_trends(df):
annual_trends = df.groupby('Year')[['MinTemp', 'MaxTemp', 'Rainfall', 'Humidity9am', 'Humidity3pm', 'Pressure9am', 'Pressure3pm']].mean()
monthly_trends = df.groupby('Month')[['MinTemp', 'MaxTemp', 'Rainfall', 'Humidity9am', 'Humidity3pm', 'Pressure9am', 'Pressure3pm']].mean()
fig, axes = plt.subplots(nrows=2, ncols=4, figsize=(20, 10))
annual_trends[['MinTemp', 'MaxTemp']].plot(ax=axes[0,0], title='Annual Avg Temperature')
annual_trends['Rainfall'].plot(ax=axes[0,1], title='Annual Avg Rainfall')
annual_trends[['Humidity9am', 'Humidity3pm']].plot(ax=axes[0,2], title='Annual Avg Humidity')
annual_trends[['Pressure9am', 'Pressure3pm']].plot(ax=axes[0,3], title='Annual Avg Pressure')
monthly_trends[['MinTemp', 'MaxTemp']].plot(ax=axes[1,0], title='Monthly Avg Temperature')
monthly_trends['Rainfall'].plot(ax=axes[1,1], title='Monthly Avg Rainfall')
monthly_trends[['Humidity9am', 'Humidity3pm']].plot(ax=axes[1,2], title='Monthly Avg Humidity')
monthly_trends[['Pressure9am', 'Pressure3pm']].plot(ax=axes[1,3], title='Monthly Avg Pressure')
plt.tight_layout()
st.pyplot(fig)
def perform_missing_value_analysis(df):
missing_values_total = df.isnull().sum()
missing_values_percentage = (df.isnull().sum() / len(df)) * 100
missing_values_analysis = pd.DataFrame({'Total Missing': missing_values_total, 'Percentage Missing': missing_values_percentage})
st.write(missing_values_analysis.sort_values(by='Percentage Missing', ascending=False))
def perform_outlier_detection(df, key_columns):
outlier_analysis = {}
for col in key_columns:
Q1 = df[col].quantile(0.25)
Q3 = df[col].quantile(0.75)
IQR = Q3 - Q1
lower_bound = Q1 - 1.5 * IQR
upper_bound = Q3 + 1.5 * IQR
outliers = df[(df[col] < lower_bound) | (df[col] > upper_bound)]
outlier_analysis[col] = {
"Outliers": outliers.shape[0],
"Percentage": (outliers.shape[0] / df.shape[0]) * 100
}
st.write(outlier_analysis)
def perform_categorical_data_analysis(df):
categorical_columns = df.select_dtypes(include=['object']).columns
categorical_analysis = {col: df[col].value_counts() for col in categorical_columns}
st.write(categorical_analysis)
# Main Function
def main():
st.title("Exploratory Data Analysis - Weather Forecasting")
# Load and preprocess data
df = load_and_preprocess_data()
# Extract temporal features
df = perform_temporal_feature_extraction(df) # This should be called before using 'Year' column
# Checkboxes and plotting functions
if st.sidebar.checkbox("Show Data Distributions"):
plot_data_distributions(df)
if st.sidebar.checkbox("Show Correlation Heatmap"):
calculate_phi_k_correlation(df)
if st.sidebar.checkbox("Show Annual and Monthly Trends"):
plot_annual_and_monthly_trends(df)
if st.sidebar.checkbox("Show Missing Value Analysis"):
perform_missing_value_analysis(df)
if st.sidebar.checkbox("Show Outlier Analysis"):
key_columns = ['MinTemp', 'MaxTemp', 'Rainfall', 'Humidity9am', 'Humidity3pm', 'Pressure9am', 'Pressure3pm']
perform_outlier_detection(df, key_columns)
if st.sidebar.checkbox("Show Categorical Data Analysis"):
perform_categorical_data_analysis(df)
if __name__ == '__main__':
main()
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