PR for Xiyuan Zhang #NhDdt1ugSGxxAr1llE5x

README.md

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-# Don't delete this repo!
+Based on the assumptions, we will need the following core functions:
+
+1. `load_data`: This function will load the Google stock price data from the CSV file using pandas.
+2. `plot_line_chart`: This function will plot a line chart of the stock prices over time using matplotlib.
+3. `plot_histogram`: This function will plot a histogram of the volume of stocks traded using matplotlib.
+4. `display_statistics`: This function will display statistical analysis of the stock prices.
+
+The entry point of the application will be `app.py`, which will contain the main logic of the Streamlit app.
+
+Let's start with the `app.py` file:
+
+app.py

all_output.txt

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+Based on the assumptions, we will need the following core functions:
+
+1. `load_data`: This function will load the Google stock price data from the CSV file using pandas.
+2. `plot_line_chart`: This function will plot a line chart of the stock prices over time using matplotlib.
+3. `plot_histogram`: This function will plot a histogram of the volume of stocks traded using matplotlib.
+4. `display_statistics`: This function will display statistical analysis of the stock prices.
+
+The entry point of the application will be `app.py`, which will contain the main logic of the Streamlit app.
+
+Let's start with the `app.py` file:
+
+app.py
+```python
+import streamlit as st
+import pandas as pd
+import matplotlib.pyplot as plt
+
+# Load data
+@st.cache
+def load_data():
+    df = pd.read_csv('google_stock_price.csv')
+    df['Date'] = pd.to_datetime(df['Date'])
+    return df
+
+# Plot line chart
+def plot_line_chart(df, start_date, end_date):
+    df = df[(df['Date'] >= start_date) & (df['Date'] <= end_date)]
+    plt.figure(figsize=(12,6))
+    plt.plot(df['Date'], df['Close'])
+    plt.title('Google Stock Prices Over Time')
+    plt.xlabel('Date')
+    plt.ylabel('Close Price')
+    st.pyplot()
+
+# Plot histogram
+def plot_histogram(df):
+    plt.figure(figsize=(12,6))
+    plt.hist(df['Volume'], bins=30, alpha=0.75)
+    plt.title('Histogram of Volume of Stocks Traded')
+    plt.xlabel('Volume')
+    plt.ylabel('Frequency')
+    st.pyplot()
+
+# Display statistics
+def display_statistics(df):
+    st.write(df.describe())
+
+def main():
+    df = load_data()
+
+    st.title('Google Stock Price Analysis')
+
+    start_date = st.date_input('Start date', df['Date'].min())
+    end_date = st.date_input('End date', df['Date'].max())
+
+    if start_date > end_date:
+        st.error('Error: End date must fall after start date.')
+    else:
+        plot_line_chart(df, start_date, end_date)
+        plot_histogram(df)
+        display_statistics(df)
+
+if __name__ == "__main__":
+    main()
+```
+
+The `requirements.txt` file will contain the necessary Python packages:
+
+requirements.txt
+```txt
+streamlit
+pandas
+matplotlib
+```
+
+This Streamlit app can be run using the command `streamlit run app.py`. The app will display a line chart of Google's stock prices over the selected date range, a histogram of the volume of stocks traded, and statistical analysis of the stock prices.

app.py

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+import streamlit as st
+import pandas as pd
+import matplotlib.pyplot as plt
+
+# Load data
+@st.cache
+def load_data():
+    df = pd.read_csv('google_stock_price.csv')
+    df['Date'] = pd.to_datetime(df['Date'])
+    return df
+
+# Plot line chart
+def plot_line_chart(df, start_date, end_date):
+    df = df[(df['Date'] >= start_date) & (df['Date'] <= end_date)]
+    plt.figure(figsize=(12,6))
+    plt.plot(df['Date'], df['Close'])
+    plt.title('Google Stock Prices Over Time')
+    plt.xlabel('Date')
+    plt.ylabel('Close Price')
+    st.pyplot()
+
+# Plot histogram
+def plot_histogram(df):
+    plt.figure(figsize=(12,6))
+    plt.hist(df['Volume'], bins=30, alpha=0.75)
+    plt.title('Histogram of Volume of Stocks Traded')
+    plt.xlabel('Volume')
+    plt.ylabel('Frequency')
+    st.pyplot()
+
+# Display statistics
+def display_statistics(df):
+    st.write(df.describe())
+
+def main():
+    df = load_data()
+
+    st.title('Google Stock Price Analysis')
+
+    start_date = st.date_input('Start date', df['Date'].min())
+    end_date = st.date_input('End date', df['Date'].max())
+
+    if start_date > end_date:
+        st.error('Error: End date must fall after start date.')
+    else:
+        plot_line_chart(df, start_date, end_date)
+        plot_histogram(df)
+        display_statistics(df)
+
+if __name__ == "__main__":
+    main()

requirements.txt

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+streamlit
+pandas
+matplotlib

run.sh

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+pip install -r requirements.txt
+
+streamlit run app.py