Depression

data/errorrates/2bit_temp/misc/processed/meancalc.py

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+import pandas as pd
+
+# List of input CSV file names
+csv_files = ['./errorrates_5_5_processed.csv', './errorrates_15_15_processed.csv', './errorrates_25_25_processed.csv', './errorrates_35_35_processed.csv', './errorrates_45_45_processed.csv', './errorrates_55_55_processed.csv']
+
+# Read all CSV files into a list of DataFrames
+dfs = [pd.read_csv(file) for file in csv_files]
+
+# Concatenate DataFrames along the columns (axis=1)
+combined_df = pd.concat(dfs, axis=1)
+
+# Calculate the mean for each row, excluding the first column which is assumed to be the index
+mean_values = combined_df.iloc[:, 1::2].mean(axis=1)
+
+# Create the output DataFrame with the original index and the calculated mean
+output_df = pd.DataFrame({
+    'Index': dfs[0].iloc[:, 0],  # Assumes the first column is the index
+    'Mean': mean_values
+})
+
+# Save the result to a new CSV file
+output_df.to_csv('errorrates_diff_0.csv', index=False)
+
+print("Row-wise mean calculated and saved.")