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Self-taught pandas data processing — much more powerful than using import csv from before.

Key points to remember:

df["Name"] automatically treats the first row as column names and returns a Series containing only that column.
idxmax() returns the index of the maximum value; max() returns the maximum value itself.
.loc is an important pandas function used to select and locate data in a DataFrame. It allows you to choose rows and columns using label-based indexing:
- Select a single row
- Select multiple rows
- Select a single column
- Select multiple columns
Syntax: df.loc[row_indexer, column_indexer]
unique() returns the count of unique values without duplicates.

# Import pandas library as pd
import pandas as pd

# Read CSV file named 'superstore_transaction.csv' and store it in a dataframe named 'df'
df = pd.read_csv("superstore_transaction.csv")

# Remove "$" and "," from the values in the 'Profit' column and convert it to integer
df["Profit"] = df["Profit"].str.replace('$', "").str.replace(",", "").astype(int)

# Remove "$" and "," from the values in the 'Sales' column and convert it to integer
df["Sales"] = df["Sales"].str.replace('$', "").str.replace(",", "").astype(int)

# Get the index of the row with the maximum value in the 'Profit' column and store it in 'col_max_profit'
col_max_profit = df["Profit"].idxmax()
# Get the index of the row with the maximum value in the 'Sales' column and store it in 'col_max_sales'
col_max_sales = df["Sales"].idxmax()

# Store the details of the transaction with highest sales
highest_sales_info = [
    "=========================\n"
    "HIGHEST SALES TRANSACTION\n"
    "=========================\n",
    "Category: {}\n".format(df.loc[col_max_sales, "Category"]),
    "Customer Name: {}\n".format(df.loc[col_max_sales, "Customer Name"]),
    "Product Name: {}\n".format(df.loc[col_max_sales, "Product Name"]),
    "Segment: {}\n".format(df.loc[col_max_sales, "Segment"]),
    "Sub-Category: {}\n".format(df.loc[col_max_sales, "Sub-Category"]),
    "Profit: {}\n".format(df["Sales"].max()),
]

# Store the details of the transaction with the highest profit
highest_profit_info = [
    "==========================\n"
    "HIGHEST PROFIT TRANSACTION\n"
    "==========================\n",
    "Category: {}\n".format(df.loc[col_max_profit, "Category"]),
    "Customer Name: {}\n".format(df.loc[col_max_profit, "Customer Name"]),
    "Product Name: {}\n".format(df.loc[col_max_profit, "Product Name"]),
    "Segment: {}\n".format(df.loc[col_max_profit, "Segment"]),
    "Sub-Category: {}\n".format(df.loc[col_max_profit, "Sub-Category"]),
    "Profit: {}\n".format(df["Profit"].max()),
]

# Open a file named 'summary_report.txt' in 'append' mode and store it in 'file'
with open("summary_report.txt", "a") as file:
    # Write the 'highest_sales_info' details to the file
    file.write(''.join(highest_sales_info))
    file.write(''.join(highest_profit_info))

import requests

url = ""
payload = {}
headers = {
    "apikey": ""
}
r = requests.request("GET", url, headers=headers, data=payload)  # API response
# view response result
print("Status code:", r.status_code)
# return content is in JSON format
# store the API response in a variable
# json() only decodes JSON-format returns
response_dict = r.json()
# process result and get response dictionary
# explore repository information — nested response_dict

f = open("summary_report.txt", "w")
head = [
    "=================================================\n"
    "SINGAPORE TO US DOLLAR EXCHANGE RATE IN REAL TIME\n"
    "=================================================\n"
]
f.writelines(head)
f.writelines([str(response_dict['info']['rate'])+"\n"])
f.close()
print("over")

import pandas as pd

df = pd.read_csv("superstore_transaction.csv")
highest_sales_info = [
    "====================\n"
    "SUPERSTORE CUSTOMERS\n"
    "====================\n",
    "TOTAL: {}\n".format(df["Customer Name"].nunique(), "Category"),
]
with open("summary_report.txt", "a") as file:
    file.writelines(highest_sales_info)

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