Pandas Data Frame, find max value and return adjacent column value, not the entire row - python-3.x

Question

New to Pandas so I'm sorry if there is an obvious solution...
I imported a CSV that only had 2 columns and I created a 3rd column.
Here's a screen shot of the top 10 rows and header:
Screen shot of DataFrame
I've figured out how to find the min and max values in the ['Amount Changed'] column but also need to pull the date associated with the min and max - but not the index and ['Profit/Loss']. I've tried iloc, loc, read about groupby - I can't get any of them to return a single value (in this case a date) that I can use again.
My goal is to create a new variable 'Gi_Date' that is in the same row as the max value in ['Amount Changed'] but tied to the date in the ['Date'] column.
I'm trying to keep the variables separate so I can use them in print statements, write them to txt files, etc.
import os
import csv
import pandas as pd
import numpy as np
#path for CSV file
csvpath = ("budget_data.csv")
#Read CSV into Panadas and give it a variable name Bank_pd
Bank_pd = pd.read_csv(csvpath, parse_dates=True)
#Number of month records in the CSV
Months = Bank_pd["Date"].count()
#Total amount of money captured in the data converted to currency
Total_Funds = '${:.0f}'.format(Bank_pd["Profit/Losses"].sum())
#Determine the amount of increase or decrease from the previous month
AmtChange = Bank_pd["Profit/Losses"].diff()
Bank_pd["Amount Changed"] = AmtChange
#Identify the greatest positive change
GreatestIncrease = '${:.0f}'.format(Bank_pd["Amount Changed"].max())
Gi_Date = Bank_pd[Bank_pd["Date"] == GreatestIncrease]
#Identify the greatest negative change
GreatestDecrease = '${:.0f}'.format(Bank_pd["Amount Changed"].min())
Gd_Date = Bank_pd[Bank_pd['Date'] == GreatestDecrease]
print(f"Total Months: {Months}")
print(f"Total: {Total_Funds}")
print(f"Greatest Increase in Profits: {Gi_Date} ({GreatestIncrease})")
print(f"Greatest Decrease in Profits: {Gd_Date} ({GreatestDecrease})")
When I run the script in git bash I don't get an error anymore so I think I'm getting close, rather than showing the date it says:
$ python PyBank.py
Total Months: 86
Total: $38382578
Greatest Increase in Profits: Empty DataFrame
Columns: [Date, Profit/Losses, Amount Changed]
Index: [] ($1926159)
Greatest Decrease in Profits: Empty DataFrame
Columns: [Date, Profit/Losses, Amount Changed]
Index: [] ($-2196167)
I want it to print out like this:
$ python PyBank.py
Total Months: 86
Total: $38382578
Greatest Increase in Profits: Feb-2012 ($1926159)
Greatest Decrease in Profits: Sept-2013 ($-2196167)
Here is one years worth of the original DataFrame:
bank_pd = pd.DataFrame({'Date':['Jan-10', 'Feb-10', 'Mar-10', 'Apl-10', 'May-10', 'Jun-10', 'Jul-10', 'Aug-10', 'Sep-10', 'Oct-10', 'Nov-10', 'Dec-10'],
'Profit/Losses':[867884, 984655, 322013, -69417, 310503, 522857, 1033096, 604885, -216386, 477532, 893810, -80353]})
The expected output with the sample df would be:
Total Months: 12
Total Funds: $5651079
Greatest Increase in Profits: Oct-10 ($693918)
Greatest Decrease in Profits: Dec-10 ($-974163)
I also had an error in the sample dataframe from above, I was missing a month when I typed it out quickly - it's fixed now.
Thanks!

Answer 1

I'm seeing few glitches in the variables used.
Bank_pd["Amount Changed"] = AmtChange
The above statement is actually replacing the dataframe with column "Amount Changed". After this statement you can use this column for any manipulation.
Below is the updated code and highlighted the newly added lines. You could add further formatting:
import pandas as pd
csvpath = ("budget_data.csv")
Bank_pd = pd.read_csv(csvpath, parse_dates=True)
inp_bank_pd = pd.DataFrame(Bank_pd)
Months = Bank_pd["Date"].count()
Total_Funds = '${:.0f}'.format(Bank_pd["Profit/Losses"].sum())
AmtChange = Bank_pd["Profit/Losses"].diff()
GreatestIncrease = Bank_pd["Amount Changed"].max()
Gi_Date = inp_bank_pd.loc[Bank_pd["Amount Changed"] == GreatestIncrease]
print(Months)
print(Total_Funds)
print(Gi_Date['Date'].values[0])
print(GreatestIncrease)

Answer 2

In your example code, Gi_date and Gd_date are trying to initialize new DF's instead of calling values. Change Gi_Date and Gd_Date:
Gi_Date = Bank_pd.sort_values('Profit/Losses').tail(1).Date
Gd_Date = Bank_pd.sort_values('Profit/Losses').head(1).Date
Check outputs:
Gi_Date
Jul-10
Gd_Date
Sep-10
To print how you want to print using string formatting:
print("Total Months: %s" %(Months))
print("Total: %s" %(Total_Funds))
print("Greatest Increase in Profits: %s %s" %(Gi_Date.to_string(index=False), GreatestIncrease))
print("Greatest Decrease in Profits: %s %s" %(Gd_Date.to_string(index=False), GreatestDecrease))
Note if you don't use the:
(Gd_Date.to_string(index=False)
The pandas object information will be included in the print output, like it is in your example when you see the DataFrame info.
Output for 12 month sample DF:
Total Months: 12
Total: $5651079
Greatest Increase in Profits: Jul-10 $693918
Greatest Decrease in Profits: Sep-10 $-974163

Answer 3

Use Series.idxmin and Series.idxmax with loc:
df.loc[df['Amount Changed'].idxmin(), 'Date']
df.loc[df['Amount Changed'].idxmax(), 'Date']
Full example based on your sample DataFrame:
df = pd.DataFrame({'Date':['Jan-2010', 'Feb-2010', 'Mar-2010', 'Apr-2010', 'May-2010',
'Jun-2010', 'Jul-2010', 'Aug-2010', 'Sep-2010', 'Oct-2010'],
'Profit/Losses': [867884,984655,322013,-69417,310503,522857,
1033096,604885,-216386,477532]})
df['Amount Changed'] = df['Profit/Losses'].diff()
print(df)
Date Profit/Losses Amount Changed
0 Jan-2010 867884 NaN
1 Feb-2010 984655 116771.0
2 Mar-2010 322013 -662642.0
3 Apr-2010 -69417 -391430.0
4 May-2010 310503 379920.0
5 Jun-2010 522857 212354.0
6 Jul-2010 1033096 510239.0
7 Aug-2010 604885 -428211.0
8 Sep-2010 -216386 -821271.0
9 Oct-2010 477532 693918.0
print(df.loc[df['Amount Changed'].idxmin(), 'Date'])
print(df.loc[df['Amount Changed'].idxmax(), 'Date'])
Sep-2010
Oct-2010