upload app

app.py

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+import pandas as pd
+import streamlit as st
+
+def transform_data(df):
+    # Transform 'respond' variable
+    respond_mapping = {
+        "Parent": 1, "Teacher": 2, "Self": 3, "Other": 4, 
+        "Significant other": 5, "Parent 1": 6, "Parent 2": 7, 
+        "Not available": 999
+    }
+    if 'respond' in df:
+        df['respond'] = df['respond'].map(respond_mapping)
+
+    # Transform 'sri_ts' and 'sld_ts' variables
+    sri_sld_values = {
+    8: 36.6, 9: 42.1, 10: 44.8, 11: 46.8, 12: 48.5, 13: 50.0, 14: 51.3,
+    15: 52.5, 16: 53.7, 17: 54.9, 18: 56.0, 19: 57.1, 20: 58.2, 21: 59.3,
+    22: 60.3, 23: 61.4, 24: 62.4, 25: 63.5, 26: 64.5, 27: 65.6, 28: 66.6,
+    29: 67.6, 30: 68.7, 31: 69.7, 32: 70.7, 33: 71.8, 34: 72.9, 35: 74.1,
+    36: 75.4, 37: 76.8, 38: 78.5, 39: 80.3, 40: 82.7
+    }
+
+    if 'sri_rs' in df.columns:
+        df['sri_ts'] = df['sri_rs'].map(sri_sld_values).fillna("NA")
+
+    if 'sld_rs' in df.columns:
+        df['sld_ts'] = df['sld_rs'].map(sri_sld_values).fillna("NA")
+
+    # Transform 'dsm_cross_ch' variables
+    dsm_cross_ch_cols = [f'dsm_cross_ch{num}' for num in range(20, 26)]
+    for col in dsm_cross_ch_cols:
+        if col in df:
+            df[col] = df[col].map({0: 2, 1: 1})
+
+    # Transform 'dsm_cross_pg' variables
+    dsm_cross_pg_cols = [f'dsm_cross_pg{num}' for num in range(20, 26)]
+    for col in dsm_cross_pg_cols:
+        if col in df:
+            df[col] = df[col].map({0: 1, 1: 2, 2: -9})
+
+    # Transform 'rcads_y' variables
+    rcads_y_cols = [f'rcads_y{num}' for num in range(14, 27)]
+    for col in rcads_y_cols:
+        if col in df:
+            df[col] = df[col] + 1
+
+    # Ensure rcads_y26 exists and set default values
+    if 'rcads_y26' not in df:
+        df['rcads_y26'] = 1
+
+    # Return transformed dataframe
+    return df
+
+def main():
+    st.title("Data Transformation App")
+
+    uploaded_file = st.file_uploader("Upload CSV or Excel file", type=['csv', 'xlsx'])
+    if uploaded_file:
+        # Determine the file type and read data accordingly
+        if uploaded_file.name.endswith('.csv'):
+            df = pd.read_csv(uploaded_file)
+        elif uploaded_file.name.endswith('.xlsx'):
+            df = pd.read_excel(uploaded_file)
+
+        # Transform the data
+        transformed_df = transform_data(df)
+
+        # Display transformed data
+        st.write("Transformed Data:")
+        st.dataframe(transformed_df)
+
+        # Download link for transformed data
+        st.download_button(
+            label="Download Transformed Data",
+            data=transformed_df.to_csv(index=False).encode('utf-8'),
+            file_name='transformed_data.csv',
+            mime='text/csv',
+        )
+
+if __name__ == '__main__':
+    main()

requirements.txt

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+streamlit
+pandas