שימו לב: על מנת להריץ את התאים ב-Live Code, יש לייבא תחילה את ספרית pandas ע”י הרצת השורת הראשונה בתא למטה.
בנוסף, נגביל את מספר השורות והעמודות שתופענה בהדפסת הטבלאות ע”י שורות הקוד השניה והשלישית:

import pandas as pd
pd.options.display.max_rows=5
pd.options.display.max_columns=5
import utils.load_auxilary_files # This will load the files used for this notebook

['orders_2_2024.csv', 'erosion.png', 'infile1.txt', 'countries-of-the-world.csv', 'monday.txt', 'StudentsGrades.csv', 'notebook_resources.zip', 'erosion_2.png', '__MACOSX', 'dog_noised.png', 'foods.txt', 'witcher_2.csv', 'dialation.png', 'evil_morty_change.png', 'ship.png', 'products2.csv', 'ex1.csv', 'evil_morty_change_3.png', 'evil_morty_1.png', 'baby.png', 'orders_3_2024.csv', 'koala.png', 'woman_noised.png', 'infile2.txt', 'orders_4_2024.csv', 'dog.png', 'witcher_1.csv', 'evil_morty_segmentation.png', 'out.txt', 'sunday.txt', 'ex2.csv', 'evil_morty_change_noised.png', 'dialation_2.png']

שאלות ממבחני עבר

Exam 2024 semester B Moed A

E.

def read_in_range(start_month,start_year,end_month,end_year):
    cur_month=start_month
    cur_year=start_year
    dfs=[]
    while cur_year < end_year or (cur_year == end_year and cur_month <= end_month):
        dfs.append(pd.read_csv("files/orders_"+str(cur_month)+"_"+str(cur_year)+".csv")) # The "files/" prefix in the path is due to technical reasons, and should be ignored.
        cur_month+=1
        if cur_month>12:
            cur_year+=1
            cur_month=1
    return pd.concat(dfs, ignore_index=True)

Read products and order tables

df_p=pd.read_csv("files/products2.csv") # The "files/" prefix in the path is due to technical reasons, and should be ignored.
df_o=read_in_range(2,2024,4,2024)

F.

display(df_o)
df_o.fillna(df_o.median(),inplace=True)
display(df_o)

	Espresso	Americano	Sandwich	Salad
0	0	1.0	0	1.0
1	1	1.0	0	NaN
...	...	...	...	...
7	1	5.0	0	0.0
8	2	0.0	5	0.0

9 rows × 4 columns

	Espresso	Americano	Sandwich	Salad
0	0	1.0	0	1.0
1	1	1.0	0	0.5
...	...	...	...	...
7	1	5.0	0	0.0
8	2	0.0	5	0.0

9 rows × 4 columns

G.

df_p.index=df_p['Name'] # First line is missing
df_join=pd.concat((df_p,df_o.T), axis=1)
df_join

	Name	Price	...	7	8
Sandwich	Sandwich	30	...	0.0	5.0
Salad	Salad	40	...	0.0	0.0
Espresso	Espresso	8	...	1.0	2.0
Americano	Americano	11	...	5.0	0.0

4 rows × 13 columns

H.

profit=df_join.iloc[:,4:].sum(axis=1)*( df_join['Price']- df_join['Cost']) # "axis of sum should be 1"
profit

Sandwich     120.0
Salad        110.0
Espresso      42.0
Americano     75.0
dtype: float64

I.

df_p['Gain']=df_o.sum()*(df_p['Price']-df_p['Cost'])
df_p.groupby('Vendor')['Gain'].sum().idxmin()

'LIV'