Data type assigned inside nested for loop isn’t as expected

I get the error:

AttributeError: ‘float’ object has no attribute ‘lower’

When trying to compile this triple nested for loop:

for row_data in df_row_list:
    for row_item_data in row_data:
        for param in search_params:
            if row_item_data.lower() == param.lower():
                row_index = df_row_list.index(row_data)

df_row_list is a list of 18 series. I am trying to iterate through it and comb through the data. How do I assign the str data type to row_item_data so that I can use the .lower() attribute?

This is kinda what the data I am working with looks like:

0         NaN        NaN  ...             NaN              NaN
1      REV. :         NC  ...             NaN              NaN
2      OP.# :  0200-00-0  ...             NaN              NaN
3         NaN        NaN  ...             NaN              NaN
4    WI ASM #   HOLDER #  ...  TOOL STICK OUT  TOOL LIFE (MIN)
5         NaN        NaN  ...            0.55              120
6         NaN        NaN  ...            0.55              120
7         NaN        NaN  ...            0.55              120
8         NaN        NaN  ...            0.55              240
9         NaN        NaN  ...            0.55              300

The search parameters are looking for series containing words such as: HOLDER DESCRIPTION, CUTTER #, Operation, TOOL DESCRIPTION I created a spreadsheet that has hundreds of options stored in it that I’m going to compare with.

I would expect it to spit it out index of the series from the df_row_list (list with a number of series in it) so that I can know where the row of data I want to use as a “Title Row” is.

Or is this not even the best way to attempt to comb through a list of series for specific keywords? I’m relatively new to python and I’m open to any help.

Answer

Just posting incase anyone has a similar issue and is looking for a different solution

This is how I found a solution:

import os
import pandas as pd

#the file path I want to pull from
in_path = r'W:R1_ManufacturingParts List ProjectTool_scrapingExcel'
#the file path where row search items are stored
search_parameters = r'W:R1_ManufacturingParts List Projectsearch_params.xlsx'
#the file I will write the dataframes to
outfile_path = r'W:R1_ManufacturingParts List Projectxlsx_reader.xlsx'

#establishing my list that I will store looped data into
file_list = []
main_header_list = []

#open the file path to store the directory in files
files = os.listdir(in_path)

#database with terms that I want to track
search = pd.read_excel(search_parameters)
length_search = search.index 

#turn search dataframe into string to do case-insensitive compare
search_string = search.to_string(header = False, index = False)

#function for case-insenitive string compare
def insensitive_compare(x1, y1):
    if x1.lower() == y1.lower():
        return True  

#function to iterate through current_file for strings and compare to 
#search_parameters to grab data column headers
def filter_data(columns, rows): #I need to fix this to stop getting that A
    for name in columns:
        for number in rows:
            cell = df.at[number, name]
            if cell == '':
                continue
            for place in length_search:
                #main compare, if str and matches search params, then do...
                if isinstance(cell, str) and insensitive_compare(search.at[place, 'Parameters'], cell) == True:
                    #this is to prevent repeats in the header list
                    if cell in header_list:
                        continue
                    else:
                        header_list.append(cell) #store data headers
                        row_list.append(number)  #store row number where it is in that data frame
                        column_list.append(name) #store column number where it is in that data frame
                else:
                    continue

#searching only for files that end with .xlsx
for file in files:
    if file.endswith('.xlsx'):
        file_list.append(in_path + '/' + file)
        
        
#read in the files to a dataframe, main loop the files will be maninpulated in
for current_file in file_list:
    df = pd.read_excel(current_file)
    
    header_list = []
    
    #get columns headers and a range for total rows
    columns = df.columns
    total_rows = df.index
    
    #adding to store where headers are stored in DF
    row_list = []
    column_list = []
    storage_list = []
    
    #add the file name to the header file so it can be separated by file
    #header_list.append(current_file)
    main_header_list.append(header_list)
    
    #running function to grab header names
    filter_data(columns, total_rows)

So now when I compile and output the data I get:

WI ASM #
HOLDER #
HOLDER DESCRIPTION
A.63.140.1/8z
A.63.140.1/8z
A.63.140.1/8z
A.63.140.1/8z
A.63.140.1/8z
CUTTER #
Harvey 980215
Harvey 980215
Harvey 28178
Harvey 28178
Harvey 74362-C3
OPERATION
GROOVE
ROUGHING
SEMI-FINISH
FINISH
DEBURR & BLEND
TOOL DESCRIPTION
CREM_.125_.015R_1
CREM_.125_.015R_2
CREM_.0781_.015R_1
CREM_.0781_.015R_2
BEM_.0625
Starting Radial Wear
-
-
-
-0.0002
-
TOOL STICK OUT
0.55
0.55
0.55
0.55
0.55
TOOL LIFE (MIN)
120
120
120
240
300

Which is cleaned up and in the order I was looking for.