solution

requirements.txt

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+# Overview 
+
+To run this file, type the following in the terminal: 
+
+python3 solution.py
+
+This program takes in the homework.csv file and performs data manipulation. It sends the output to solution.csv. If you want to change the input file, you need to do that within the code. 
+
+# Dependencies 
+
+To run this program, you need the following
+
+1. numpy library 
+2. pandas library 
+

solution.py

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+## Goal: Code to analyze and transform data
+## Input: Code reads data from `homework.csv` file passed in line 9
+## Output: Code produces parsed output file to `solution.csv`
+## Code by: Anna Fritz 
+
+import pandas as pd
+import numpy as np
+
+#################################
+# First thing is reading the data 
+data= pd.read_csv("homework.csv", low_memory=False)
+data.columns = data.columns.str.lower()
+
+# we want to index on the item number 
+data.set_index('item number', inplace=True)
+
+#############################
+#Change dates to use ISO 8601
+def format_dates(df): 
+    currency_columns = df.filter(like='date').columns
+    for c in currency_columns: 
+        data[c] = pd.to_datetime(data[c], format='%m/%d/%y')
+        data[c] = data[c].apply(lambda x: x.isoformat())
+    return df
+
+data = format_dates(data)
+
+##################################################
+# Currency should be rounded to unit of accounting. 
+# Assume USD for currency and round to cents.
+def format_currency(df, c):
+    df[c] = pd.to_numeric(df[c].replace('[\$,]', '', regex=True), errors='coerce')
+    df[c] = df[c].round(2)
+    df[c] = df[c].map('${:,.2f}'.format)
+    return df
+
+# Now we need to find which columns may contain a currency and format those 
+def apply_format_currency(df):
+    # Select columns that contain '($)' in the title
+    currency_columns = df.filter(like='($)').columns
+    # Apply the format_currency function to the selected columns
+    for c in currency_columns: 
+        df = format_currency(df,c)
+    return df
+
+data = apply_format_currency(data)
+
+###################################
+# convert any measurements to inches 
+# For sake of the project, lets say things may be in inches, feet, or centimeters. The column title will include the label. 
+# So we need to get the column that may contain length and convert them to inches 
+def convert_dimensions_to_inches(df):
+    conversion_factors_length = {'inches': 1, 'feet': 12, 'centimeters': 0.393701}
+
+    # Identify columns with titles containing conversion factors
+    dimension_columns = []
+    for key, val in conversion_factors_length.items():
+        dimension_columns.extend(df.filter(like=key).columns)
+
+    # Convert to inches using conversion factors
+    for column in dimension_columns:
+        for key, val in conversion_factors_length.items():
+            if key in column:
+                df[column] = df[column].apply(lambda x: x * val)
+    return df
+
+data = convert_dimensions_to_inches(data)
+
+##############################
+# convert any weights to pounds
+# Same thought process but now for weight... if you want to add more conversion factors you would just add them to the dictionary 
+def convert_dimensions_to_pounds(df):
+    conversion_factors_weight = {'pounds': 1, 'kilograms': 2.20462}
+
+    # Identify columns with titles containing conversion factors
+    dimension_columns = []
+    for key, val in conversion_factors_weight.items():
+        dimension_columns.extend(df.filter(like=key).columns)
+
+    # Convert to inches using conversion factors
+    for column in dimension_columns:
+        for key, val in conversion_factors_weight.items():
+            if key in column:
+                df[column] = df[column].apply(lambda x: x * val)
+    return df
+
+data = convert_dimensions_to_pounds(data)
+
+#######################################################
+# handle UPC / Gtin / EAN should be handled as strings
+def convert_lab_to_str(df):
+    possible_columns = ['upc', 'gtin', 'ean']
+    for column in possible_columns:
+        if column in df.columns: 
+            df[column] = df[column].astype(str)
+    return df
+
+data = convert_lab_to_str(data)
+
+# floating points should be preserved 
+
+#####################
+# output file to csv
+data.to_csv("solution.csv")