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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,48 +1,79 @@ | ||
| import csv | ||
| import streamlit as st | ||
| import pandas as pd | ||
|
|
||
| def sum_csv_part(filename, start_row, end_row, start_col, end_col): | ||
| with open(filename, 'r') as file: | ||
| csv_reader = csv.reader(file) | ||
| data = list(csv_reader) | ||
|
|
||
| # Ensure start_row and end_row are within the range of rows | ||
| start_row = max(0, min(start_row, len(data)-1)) | ||
| end_row = max(start_row, min(end_row, len(data)-1)) | ||
|
|
||
| # Ensure start_col and end_col are within the range of columns | ||
| start_col = max(0, min(start_col, len(data[0])-1)) | ||
| end_col = max(start_col, min(end_col, len(data[0])-1)) | ||
|
|
||
| sums = [] | ||
| for i in range(start_row, end_row + 1): | ||
| row_sum = 0 # Initialize sum variable for each row | ||
| for j in range(start_col, end_col + 1): | ||
| try: | ||
| row_sum += float(data[i][j]) # Accumulate values for each row | ||
| except ValueError: | ||
| print("ValueError: Invalid literal for float():", data[i][j]) | ||
| print("Sum of values in row", i+1, ":", row_sum) | ||
| row_sum = sum(float(data[i][j]) for j in range(start_col, end_col + 1)) | ||
| sums.append(row_sum) | ||
| return sums | ||
|
|
||
| def multiply_csv_part(filename, start_row, end_row, start_col, end_col): | ||
| with open(filename, 'r') as file: | ||
| csv_reader = csv.reader(file) | ||
| data = list(csv_reader) | ||
|
|
||
| # Ensure start_row and end_row are within the range of rows | ||
| start_row = max(0, min(start_row, len(data)-1)) | ||
| end_row = max(start_row, min(end_row, len(data)-1)) | ||
|
|
||
| # Ensure start_col and end_col are within the range of columns | ||
| start_col = max(0, min(start_col, len(data[0])-1)) | ||
| end_col = max(start_col, min(end_col, len(data[0])-1)) | ||
|
|
||
| products = [] | ||
| for i in range(start_row, end_row + 1): | ||
| row_product = 1 # Initialize product variable for each row as 1 | ||
| for j in range(start_col, end_col + 1): | ||
| try: | ||
| row_product *= float(data[i][j]) # Accumulate values for each row | ||
| except ValueError: | ||
| print("ValueError: Invalid literal for float():", data[i][j]) | ||
| print("Product of values in row", i+1, ":", row_product) | ||
|
|
||
| sum_csv_part('data.csv', 1, 3, 6, 7) | ||
| multiply_csv_part('data.csv', 1, 3, 3, 4) | ||
| row_product = float(data[i][start_col]) * float(data[i][end_col]) | ||
| products.append(row_product) | ||
| return products | ||
|
|
||
| def divide_csv_part(filename, start_row, end_row, start_col, end_col): | ||
| sums = sum_csv_part(filename, start_row, end_row, start_col, end_col) | ||
| products_1 = multiply_csv_part(filename, start_row, end_row, 3, 4) | ||
| products_2 = multiply_csv_part(filename, start_row, end_row, 3, 5) | ||
|
|
||
| division_results_1 = [] | ||
| division_results_2 = [] | ||
| for sum_val, prod_val in zip(sums, products_1): | ||
| if sum_val != 0: | ||
| division_results_2.append(prod_val / sum_val) | ||
| else: | ||
| division_results_2.append(float('NaN')) | ||
|
|
||
| for sum_val, prod_val in zip(sums, products_2): | ||
| if sum_val != 0: | ||
| division_results_1.append(prod_val / sum_val) | ||
| else: | ||
| division_results_1.append(float('NaN')) | ||
|
|
||
| st.title("Swing Efficiency %") | ||
|
|
||
| a = {'Names': print_csv_row_range(filename, 1, 135),'Swing Efficiency %': division_results_1} | ||
| df = pd.DataFrame.from_dict(a, orient='index') | ||
| df = df.transpose() | ||
|
|
||
| sort_column = st.selectbox('Select column to sort by:', df.columns) | ||
| df_sorted = df.sort_values(by=sort_column, ascending=False) | ||
|
|
||
| st.table(df_sorted) | ||
|
|
||
| def print_csv_row_range(filename, start_row_index, end_row_index): | ||
| with open(filename, 'r') as file: | ||
| csv_reader = csv.reader(file) | ||
| rows = list(csv_reader) | ||
| result = [] | ||
| for row_index in range(start_row_index, min(end_row_index + 1, len(rows))): | ||
| result.append(rows[row_index][0]) # Exclude the last element of the row | ||
| return result | ||
|
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||
|
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| divide_csv_part('data.csv', 1, 135, 6, 7) |