Python Pandas Data Filtering and Mean Conductivity Calculation

This Python code utilizes the numpy and pandas libraries to analyze and process data from an Excel file. Here's a breakdown of the code's functionality:

1. Importing Libraries:

   import numpy as np
   import pandas as pd
   

   This section imports the numpy library for numerical operations and the pandas library for data manipulation, primarily working with DataFrames.

2. Reading Excel Data:

   df = pd.read_excel('2023_2_23No1_data.xlsx', sheet_name='Sheet', index_col=0)
   

   This line reads data from an Excel file named '2023_2_23No1_data.xlsx'. It specifies the sheet named 'Sheet' within the file and sets the first column as the index.

3. Defining Indices:

   index = [1, 2, 3, 7, 11, 17, 23, 31, 43, 61, 89, 127, 179, 251, 349]
   

   This line creates a list named index containing specific numerical values. These values will be used for filtering data later in the code.

4. Initializing Variables:

   my_df = 'df'
   df_name = df
   

   These lines initialize two variables:

   • my_df: This variable is initially set to the string 'df', which will later hold a list of filtered DataFrames.
   • df_name: This variable is assigned the original DataFrame read from the Excel file.

5. Filtering Data based on Indices:

   for i in range(len(index)):
       the_df = df_name + str(index[i])
       the_df = df[df['name'] == 'peritumor'] & (df['frequency'] == index) & (df['conductivity'] > 0)
       my_df.append(the_df)
   

   This section uses a loop to iterate through the index list. For each index value (i):

   • the_df = df_name + str(index[i]): This line creates a new DataFrame name by combining df_name with the current index value as a string.
   • the_df = df[df['name'] == 'peritumor'] & (df['frequency'] == index) & (df['conductivity'] > 0): This line filters the original DataFrame df based on the following criteria:
     • df['name'] == 'peritumor': Selects rows where the 'name' column value is 'peritumor'.
     • df['frequency'] == index: Selects rows where the 'frequency' column value matches the current index value from the index list.
     • df['conductivity'] > 0: Selects rows where the 'conductivity' column value is greater than 0.
   • my_df.append(the_df): Appends the filtered DataFrame the_df to the my_df list.

6. Calculating Mean Conductivity:

   for item  in my_df:
       print((sum(item['conductivity']))/(item.shape[0]))
   

   This final loop iterates through each DataFrame in the my_df list. For each DataFrame (item):

   • sum(item['conductivity']): Calculates the sum of all conductivity values in the DataFrame.
   • item.shape[0]: Gets the number of rows in the DataFrame (total number of data points).
   • (sum(item['conductivity']))/(item.shape[0]): Divides the sum of conductivity values by the number of rows to calculate the mean conductivity for that DataFrame.
   • print(...): Prints the calculated mean conductivity value for each DataFrame in the my_df list.

In essence, this code reads data from an Excel file, filters it based on specific conditions (name, frequency, and conductivity), and then calculates and prints the mean conductivity for each filtered dataset.