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Update app.py
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app.py
CHANGED
@@ -5,6 +5,8 @@ import seaborn as sns
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import numpy as np
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from io import StringIO
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import openpyxl
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def load_data(file):
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file_extension = file.name.split('.')[-1].lower()
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@@ -24,22 +26,51 @@ def manual_data_entry():
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if col_names:
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num_rows = st.number_input("Enter number of rows:", min_value=1, value=5)
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data =
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for i in range(num_rows):
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row = []
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for col in col_names:
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value = st.text_input(f"Enter value for {col} (Row {i+1}):")
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row.append(value)
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data.append(row)
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return None
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def
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st.
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# Summary statistics
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st.write("Summary Statistics:")
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@@ -70,19 +101,28 @@ def perform_analysis(data):
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sns.histplot(data[column], kde=True, ax=ax)
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st.pyplot(fig)
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st.
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#
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st.
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plan = st.text_area("Describe your plan:")
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# Data
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st.header("3. Data")
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data_input_method = st.radio("Choose data input method:", ("Upload File", "Manual Entry"))
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if data_input_method == "Upload File":
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@@ -98,18 +138,8 @@ def main():
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st.write("Data Preview:")
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st.write(data.head())
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for col in data.columns:
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try:
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data[col] = pd.to_numeric(data[col])
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except ValueError:
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pass # Keep as non-numeric if conversion fails
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perform_analysis(data)
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# Conclusion
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st.header("5. Conclusion")
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conclusion = st.text_area("Write your conclusion based on the analysis:")
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if __name__ == "__main__":
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main()
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import numpy as np
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from io import StringIO
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import openpyxl
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from st_aggrid import AgGrid, GridUpdateMode
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from st_aggrid.grid_options_builder import GridOptionsBuilder
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def load_data(file):
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file_extension = file.name.split('.')[-1].lower()
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if col_names:
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num_rows = st.number_input("Enter number of rows:", min_value=1, value=5)
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data = pd.DataFrame(columns=col_names, index=range(num_rows))
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gd = GridOptionsBuilder.from_dataframe(data)
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gd.configure_default_column(editable=True)
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gridoptions = gd.build()
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grid_table = AgGrid(data, gridOptions=gridoptions,
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update_mode=GridUpdateMode.VALUE_CHANGED,
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height=400)
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return grid_table['data']
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return None
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def preprocess_data(data):
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st.subheader("Data Preprocessing")
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# Handle missing values
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if data.isnull().sum().sum() > 0:
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st.write("Handling missing values:")
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for column in data.columns:
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if data[column].isnull().sum() > 0:
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method = st.selectbox(f"Choose method for {column}:",
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["Drop", "Fill with mean", "Fill with median", "Fill with mode"])
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if method == "Drop":
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data = data.dropna(subset=[column])
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elif method == "Fill with mean":
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data[column].fillna(data[column].mean(), inplace=True)
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elif method == "Fill with median":
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data[column].fillna(data[column].median(), inplace=True)
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elif method == "Fill with mode":
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data[column].fillna(data[column].mode()[0], inplace=True)
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# Convert data types
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for column in data.columns:
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if data[column].dtype == 'object':
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try:
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data[column] = pd.to_numeric(data[column])
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st.write(f"Converted {column} to numeric.")
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except ValueError:
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st.write(f"Kept {column} as categorical.")
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return data
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def perform_analysis(data):
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st.header("Exploratory Data Analysis")
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# Summary statistics
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st.write("Summary Statistics:")
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sns.histplot(data[column], kde=True, ax=ax)
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st.pyplot(fig)
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# Box plots for numerical columns
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st.write("Box Plots:")
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for column in numeric_data.columns:
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fig, ax = plt.subplots()
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sns.boxplot(data=data, y=column, ax=ax)
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st.pyplot(fig)
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# Bar plots for categorical columns
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categorical_columns = data.select_dtypes(include=['object']).columns
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if not categorical_columns.empty:
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st.write("Bar Plots for Categorical Variables:")
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for column in categorical_columns:
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fig, ax = plt.subplots()
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data[column].value_counts().plot(kind='bar', ax=ax)
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plt.title(f"Distribution of {column}")
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plt.xlabel(column)
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plt.ylabel("Count")
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st.pyplot(fig)
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def main():
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st.title("Interactive EDA Toolkit")
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data_input_method = st.radio("Choose data input method:", ("Upload File", "Manual Entry"))
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if data_input_method == "Upload File":
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st.write("Data Preview:")
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st.write(data.head())
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data = preprocess_data(data)
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perform_analysis(data)
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if __name__ == "__main__":
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main()
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