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Intro to Pandas for Data Analysis

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Exploring DataFrames with Currency Data

Finished

April 18, 2025 4:45 PM

Elapsed time (min)

Completed activities

Resolution

Activities

Project.ipynb

Notebook

In [1]:

import pandas as pd
df = pd.read_csv("currencies.csv")
df

Out[1]:

	Name	Symbol	Code	Countries	Digits	Number
0	UK Pound	£	GBP	Guernsey,Isle Of Man,Jersey,United Kingdom Of ...	2.0	826.0
1	Czech Koruna	Kč	CZK	Czechia	2.0	203.0
2	Latvian Lat	Ls	LVL	NaN	NaN	NaN
3	Swiss Franc	CHF	CHF	Liechtenstein,Switzerland	2.0	756.0
4	Croatian Kruna	kn	HRK	Croatia	2.0	191.0
5	Danish Krone	kr	DKK	Denmark,Faroe Islands (The),Greenland	2.0	208.0
6	Korean Won	₩	KRW	Korea (The Republic Of)	0.0	410.0
7	Swedish Krona	kr	SEK	Sweden	2.0	752.0
8	Turkish Lira	₤	TRY	Turkey	2.0	949.0
9	Hungarian Forint	Ft	HUF	Hungary	2.0	348.0
10	Brazilian Real	R$	BRL	Brazil	2.0	986.0
11	Lithuanian Litas	Lt	LTL	NaN	NaN	NaN
12	Bulgarian Lev	лB	BGN	Bulgaria	2.0	975.0
13	Polish Zloty	zł	PLN	Poland	2.0	985.0
14	US Dollar	$	USD	American Samoa,Bonaire, Sint Eustatius And Sab...	2.0	840.0
15	Russian Ruble	руб	RUB	Russian Federation (The)	2.0	643.0
16	Japanese Yen	¥	JPY	Japan	0.0	392.0
17	Romanian New Leu	lei	RON	Romania	2.0	946.0
18	Norwegian Krone	kr	NOK	Bouvet Island,Norway,Svalbard And Jan Mayen	2.0	578.0
19	Australian Dollar	$	AUD	Australia,Christmas Island,Cocos (Keeling) Isl...	2.0	36.0
20	Israeli Shekel	₪	ILS	Israel	2.0	376.0
21	New Zealand Dollar	$	NZD	Cook Islands (The),New Zealand,Niue,Pitcairn,T...	2.0	554.0
22	Indonesian Rupiah	Rp	IDR	Indonesia	2.0	360.0
23	Philippine Peso	₱	PHP	Philippines (The)	2.0	608.0
24	Euro	€	EUR	Andorra,Austria,Belgium,Cyprus,Estonia,Europea...	2.0	978.0
25	Canadian Dollar	$	CAD	Canada	2.0	124.0
26	Chinese Yuan	¥	CNY	China	2.0	156.0
27	Hong Kong Dollar	$	HKD	Hong Kong	2.0	344.0
28	Indian Rupee	₨	INR	Bhutan,India	2.0	356.0
29	Mexican Peso	$	MXN	Mexico	2.0	484.0
30	Malaysian Ringgit	RM	MYR	Malaysia	2.0	458.0
31	South African Rand	R	ZAR	Lesotho,Namibia,South Africa	2.0	710.0
32	Thai Baht	฿	THB	Thailand	2.0	764.0
33	Singaporean Dollar	$	SGD	Singapore	2.0	702.0

Activities¶

1. Getting an Overview of the dataset¶

In [2]:

# try your code here
df.dtypes

Out[2]:

Name          object
Symbol        object
Code          object
Countries     object
Digits       float64
Number       float64
dtype: object

2. Select the correct ansewrs for dataset information¶

In [3]:

# try your code here
df['Digits'].mean(), df['Digits'].std()

Out[3]:

(1.875, 0.4918693768379647)

3. Get the statistics of the dataset¶

In [4]:

# try your code here

Out[4]:

4. Count the number of unique currencies in the dataset¶

In [ ]:

# try your code here

5. Identify the number of missing values in each column¶

In [6]:

# try your code here
sum(df.isna().sum())

Out[6]:

6. Determine the highest currency number in the dataset¶

In [7]:

# try your code here
df['Number'].max()

Out[7]:

986.0

7. Select Currency Names¶

In [8]:

# try your code here
names = df['Name']

8. Get the details of the 3rd row¶

In [10]:

df.head()

Out[10]:

	Name	Symbol	Code	Countries	Digits	Number
0	UK Pound	£	GBP	Guernsey,Isle Of Man,Jersey,United Kingdom Of ...	2.0	826.0
1	Czech Koruna	Kč	CZK	Czechia	2.0	203.0
2	Latvian Lat	Ls	LVL	NaN	NaN	NaN
3	Swiss Franc	CHF	CHF	Liechtenstein,Switzerland	2.0	756.0
4	Croatian Kruna	kn	HRK	Croatia	2.0	191.0

In [11]:

# try your code here
row_3 = df.iloc[2]
row_3

Out[11]:

Name         Latvian Lat
Symbol                Ls
Code                 LVL
Countries            NaN
Digits               NaN
Number               NaN
Name: 2, dtype: object

9. Select rows 10th to 15th (inclusive) from the DataFrame¶

In [13]:

# try your code here
rows = df.iloc[9:15]

10. Extract Alternating Rows from DataFrame¶

In [15]:

# try your code here
rows_every_other = df.iloc[0:len(df):2]

11. Select specific columns¶

In [19]:

df.head(7)

Out[19]:

	Name	Symbol	Code	Countries	Digits	Number
0	UK Pound	£	GBP	Guernsey,Isle Of Man,Jersey,United Kingdom Of ...	2.0	826.0
1	Czech Koruna	Kč	CZK	Czechia	2.0	203.0
2	Latvian Lat	Ls	LVL	NaN	NaN	NaN
3	Swiss Franc	CHF	CHF	Liechtenstein,Switzerland	2.0	756.0
4	Croatian Kruna	kn	HRK	Croatia	2.0	191.0
5	Danish Krone	kr	DKK	Denmark,Faroe Islands (The),Greenland	2.0	208.0
6	Korean Won	₩	KRW	Korea (The Republic Of)	0.0	410.0

In [27]:

# try your code here
cols = df.iloc[:, [2,4,5]]
cols

Out[27]:

	Code	Digits	Number
0	GBP	2.0	826.0
1	CZK	2.0	203.0
2	LVL	NaN	NaN
3	CHF	2.0	756.0
4	HRK	2.0	191.0
5	DKK	2.0	208.0
6	KRW	0.0	410.0
7	SEK	2.0	752.0
8	TRY	2.0	949.0
9	HUF	2.0	348.0
10	BRL	2.0	986.0
11	LTL	NaN	NaN
12	BGN	2.0	975.0
13	PLN	2.0	985.0
14	USD	2.0	840.0
15	RUB	2.0	643.0
16	JPY	0.0	392.0
17	RON	2.0	946.0
18	NOK	2.0	578.0
19	AUD	2.0	36.0
20	ILS	2.0	376.0
21	NZD	2.0	554.0
22	IDR	2.0	360.0
23	PHP	2.0	608.0
24	EUR	2.0	978.0
25	CAD	2.0	124.0
26	CNY	2.0	156.0
27	HKD	2.0	344.0
28	INR	2.0	356.0
29	MXN	2.0	484.0
30	MYR	2.0	458.0
31	ZAR	2.0	710.0
32	THB	2.0	764.0
33	SGD	2.0	702.0

12. Select first three columns¶

In [26]:

# try your code here
cols_first_three = df.loc[:, ['Name', 'Symbol']]
cols_first_three

Out[26]:

	Name	Symbol
0	UK Pound	£
1	Czech Koruna	Kč
2	Latvian Lat	Ls
3	Swiss Franc	CHF
4	Croatian Kruna	kn
5	Danish Krone	kr
6	Korean Won	₩
7	Swedish Krona	kr
8	Turkish Lira	₤
9	Hungarian Forint	Ft
10	Brazilian Real	R$
11	Lithuanian Litas	Lt
12	Bulgarian Lev	лB
13	Polish Zloty	zł
14	US Dollar	$
15	Russian Ruble	руб
16	Japanese Yen	¥
17	Romanian New Leu	lei
18	Norwegian Krone	kr
19	Australian Dollar	$
20	Israeli Shekel	₪
21	New Zealand Dollar	$
22	Indonesian Rupiah	Rp
23	Philippine Peso	₱
24	Euro	€
25	Canadian Dollar	$
26	Chinese Yuan	¥
27	Hong Kong Dollar	$
28	Indian Rupee	₨
29	Mexican Peso	$
30	Malaysian Ringgit	RM
31	South African Rand	R
32	Thai Baht	฿
33	Singaporean Dollar	$

The End!¶

Strip Solutions.ipynb

Notebook

In [1]:

import re
import json
from pathlib import Path

In [2]:

pattern = re.compile("(#*)\s*")

In [3]:

def is_control_cell(source):
    control_lines = ["solution", "assertion"]
    for control_line in control_lines:
        if source.lower().startswith(control_line):
            return True
    return False

Configurations:

In [4]:

CHECK_OVERWRITE = True
SOLUTION_NOTEBOOK_NAME = 'Solution.ipynb'
NEW_NOTEBOOK_NAME = 'Project.ipynb'

In [5]:

assert Path(SOLUTION_NOTEBOOK_NAME).exists(), f"The solution notebook '{SOLUTION_NOTEBOOK_NAME}' doesn't exist"

with open(SOLUTION_NOTEBOOK_NAME) as fp:
    notebook = json.load(fp)

cells = notebook['cells']

new_cells = []
control_cut = False

for cell in cells:
    cell_type = cell['cell_type']

    if control_cut:
        if cell_type == 'markdown' and cell['source'] and pattern.match(cell['source'][0]):
            control_cut = False
        else:
            continue
        
    if cell_type == 'markdown' and cell['source']:
        if is_control_cell(cell['source'][0]):
            control_cut = True
            continue

    new_cells.append(cell)

notebook['cells'] = new_cells

if Path(NEW_NOTEBOOK_NAME).exists():
    answer = input(f"You're about to overwrite {NEW_NOTEBOOK_NAME}. Are you sure? (y/N)") or 'n'
    if answer.lower() != 'y':
        assert False, "Cancelled."

with open(NEW_NOTEBOOK_NAME, 'w') as fp:
    json.dump(notebook, fp, indent=2)

print(f"\nNew notebook saved in: {NEW_NOTEBOOK_NAME}")

New notebook saved in: Project.ipynb

In [ ]:

Solution.ipynb

Notebook

In [1]:

import pandas as pd
df = pd.read_csv("currencies.csv")
df

Out[1]:

	Name	Symbol	Code	Countries	Digits	Number
0	UK Pound	£	GBP	Guernsey,Isle Of Man,Jersey,United Kingdom Of ...	2.0	826.0
1	Czech Koruna	Kč	CZK	Czechia	2.0	203.0
2	Latvian Lat	Ls	LVL	NaN	NaN	NaN
3	Swiss Franc	CHF	CHF	Liechtenstein,Switzerland	2.0	756.0
4	Croatian Kruna	kn	HRK	Croatia	2.0	191.0
5	Danish Krone	kr	DKK	Denmark,Faroe Islands (The),Greenland	2.0	208.0
6	Korean Won	₩	KRW	Korea (The Republic Of)	0.0	410.0
7	Swedish Krona	kr	SEK	Sweden	2.0	752.0
8	Turkish Lira	₤	TRY	Turkey	2.0	949.0
9	Hungarian Forint	Ft	HUF	Hungary	2.0	348.0
10	Brazilian Real	R$	BRL	Brazil	2.0	986.0
11	Lithuanian Litas	Lt	LTL	NaN	NaN	NaN
12	Bulgarian Lev	лB	BGN	Bulgaria	2.0	975.0
13	Polish Zloty	zł	PLN	Poland	2.0	985.0
14	US Dollar	$	USD	American Samoa,Bonaire, Sint Eustatius And Sab...	2.0	840.0
15	Russian Ruble	руб	RUB	Russian Federation (The)	2.0	643.0
16	Japanese Yen	¥	JPY	Japan	0.0	392.0
17	Romanian New Leu	lei	RON	Romania	2.0	946.0
18	Norwegian Krone	kr	NOK	Bouvet Island,Norway,Svalbard And Jan Mayen	2.0	578.0
19	Australian Dollar	$	AUD	Australia,Christmas Island,Cocos (Keeling) Isl...	2.0	36.0
20	Israeli Shekel	₪	ILS	Israel	2.0	376.0
21	New Zealand Dollar	$	NZD	Cook Islands (The),New Zealand,Niue,Pitcairn,T...	2.0	554.0
22	Indonesian Rupiah	Rp	IDR	Indonesia	2.0	360.0
23	Philippine Peso	₱	PHP	Philippines (The)	2.0	608.0
24	Euro	€	EUR	Andorra,Austria,Belgium,Cyprus,Estonia,Europea...	2.0	978.0
25	Canadian Dollar	$	CAD	Canada	2.0	124.0
26	Chinese Yuan	¥	CNY	China	2.0	156.0
27	Hong Kong Dollar	$	HKD	Hong Kong	2.0	344.0
28	Indian Rupee	₨	INR	Bhutan,India	2.0	356.0
29	Mexican Peso	$	MXN	Mexico	2.0	484.0
30	Malaysian Ringgit	RM	MYR	Malaysia	2.0	458.0
31	South African Rand	R	ZAR	Lesotho,Namibia,South Africa	2.0	710.0
32	Thai Baht	฿	THB	Thailand	2.0	764.0
33	Singaporean Dollar	$	SGD	Singapore	2.0	702.0

Activities¶

1. Getting an Overview of the dataset¶

In [2]:

# try your code here

Solution:

In [3]:

df.head()

Out[3]:

	Name	Symbol	Code	Countries	Digits	Number
0	UK Pound	£	GBP	Guernsey,Isle Of Man,Jersey,United Kingdom Of ...	2.0	826.0
1	Czech Koruna	Kč	CZK	Czechia	2.0	203.0
2	Latvian Lat	Ls	LVL	NaN	NaN	NaN
3	Swiss Franc	CHF	CHF	Liechtenstein,Switzerland	2.0	756.0
4	Croatian Kruna	kn	HRK	Croatia	2.0	191.0

Assertions:

In [4]:

# Multiple Choice Question

2. Select the correct ansewrs for dataset information¶

In [5]:

# try your code here

Solution:

In [16]:

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 34 entries, 0 to 33
Data columns (total 6 columns):
 #   Column     Non-Null Count  Dtype  
---  ------     --------------  -----  
 0   Name       34 non-null     object 
 1   Symbol     34 non-null     object 
 2   Code       34 non-null     object 
 3   Countries  32 non-null     object 
 4   Digits     32 non-null     float64
 5   Number     32 non-null     float64
dtypes: float64(2), object(4)
memory usage: 1.7+ KB

Assertions:

In [ ]:

3. Get the statistics of the dataset¶

In [3]:

# try your code here

Solution:

In [17]:

df.describe()

Out[17]:

	Digits	Number
count	32.000000	32.000000
mean	1.875000	562.437500
std	0.491869	290.540313
min	0.000000	36.000000
25%	2.000000	354.000000
50%	2.000000	566.000000
75%	2.000000	779.500000
max	2.000000	986.000000

Assertions:

In [ ]:

4. Count the number of unique currencies in the dataset¶

In [4]:

# try your code here

Solution:

In [ ]:

Assertions:

In [ ]:

5. Identify the number of missing values in each column¶

In [5]:

# try your code here

Solution:

In [6]:

df.isnull().sum()

Out[6]:

Name         0
Symbol       0
Code         0
Countries    2
Digits       2
Number       2
dtype: int64

Assertions:

In [ ]:

6. Determine the highest currency number in the dataset¶

In [6]:

# try your code here

Solution:

In [ ]:

Assertions:

In [ ]:

7. Select Currency Names¶

In [7]:

# try your code here
names = ...

Solution:

In [ ]:

Assertions:

In [ ]:

8. Get the details of the 3rd row¶

In [8]:

# try your code here
row_3 = ...

Solution:

In [ ]:

Assertions:

In [ ]:

9. Select rows 10 to 15 (inclusive) from the DataFrame¶

In [9]:

# try your code here
rows = ...

Solution:

In [7]:

df

Out[7]:

	Name	Symbol	Code	Countries	Digits	Number
0	UK Pound	£	GBP	Guernsey,Isle Of Man,Jersey,United Kingdom Of ...	2.0	826.0
1	Czech Koruna	Kč	CZK	Czechia	2.0	203.0
2	Latvian Lat	Ls	LVL	NaN	NaN	NaN
3	Swiss Franc	CHF	CHF	Liechtenstein,Switzerland	2.0	756.0
4	Croatian Kruna	kn	HRK	Croatia	2.0	191.0
5	Danish Krone	kr	DKK	Denmark,Faroe Islands (The),Greenland	2.0	208.0
6	Korean Won	₩	KRW	Korea (The Republic Of)	0.0	410.0
7	Swedish Krona	kr	SEK	Sweden	2.0	752.0
8	Turkish Lira	₤	TRY	Turkey	2.0	949.0
9	Hungarian Forint	Ft	HUF	Hungary	2.0	348.0
10	Brazilian Real	R$	BRL	Brazil	2.0	986.0
11	Lithuanian Litas	Lt	LTL	NaN	NaN	NaN
12	Bulgarian Lev	лB	BGN	Bulgaria	2.0	975.0
13	Polish Zloty	zł	PLN	Poland	2.0	985.0
14	US Dollar	$	USD	American Samoa,Bonaire, Sint Eustatius And Sab...	2.0	840.0
15	Russian Ruble	руб	RUB	Russian Federation (The)	2.0	643.0
16	Japanese Yen	¥	JPY	Japan	0.0	392.0
17	Romanian New Leu	lei	RON	Romania	2.0	946.0
18	Norwegian Krone	kr	NOK	Bouvet Island,Norway,Svalbard And Jan Mayen	2.0	578.0
19	Australian Dollar	$	AUD	Australia,Christmas Island,Cocos (Keeling) Isl...	2.0	36.0
20	Israeli Shekel	₪	ILS	Israel	2.0	376.0
21	New Zealand Dollar	$	NZD	Cook Islands (The),New Zealand,Niue,Pitcairn,T...	2.0	554.0
22	Indonesian Rupiah	Rp	IDR	Indonesia	2.0	360.0
23	Philippine Peso	₱	PHP	Philippines (The)	2.0	608.0
24	Euro	€	EUR	Andorra,Austria,Belgium,Cyprus,Estonia,Europea...	2.0	978.0
25	Canadian Dollar	$	CAD	Canada	2.0	124.0
26	Chinese Yuan	¥	CNY	China	2.0	156.0
27	Hong Kong Dollar	$	HKD	Hong Kong	2.0	344.0
28	Indian Rupee	₨	INR	Bhutan,India	2.0	356.0
29	Mexican Peso	$	MXN	Mexico	2.0	484.0
30	Malaysian Ringgit	RM	MYR	Malaysia	2.0	458.0
31	South African Rand	R	ZAR	Lesotho,Namibia,South Africa	2.0	710.0
32	Thai Baht	฿	THB	Thailand	2.0	764.0
33	Singaporean Dollar	$	SGD	Singapore	2.0	702.0

In [10]:

rows = df.loc[9:14]

In [11]:

rows

Out[11]:

	Name	Symbol	Code	Countries	Digits	Number
9	Hungarian Forint	Ft	HUF	Hungary	2.0	348.0
10	Brazilian Real	R$	BRL	Brazil	2.0	986.0
11	Lithuanian Litas	Lt	LTL	NaN	NaN	NaN
12	Bulgarian Lev	лB	BGN	Bulgaria	2.0	975.0
13	Polish Zloty	zł	PLN	Poland	2.0	985.0
14	US Dollar	$	USD	American Samoa,Bonaire, Sint Eustatius And Sab...	2.0	840.0

Assertions:

In [12]:

assert 'rows' in globals(), "Variable 'rows' does not exist."
assert isinstance(rows, pd.DataFrame), "Variable 'rows' is not a DataFrame."
expected_rows = df.loc[9:14]
try:
    pd.testing.assert_frame_equal(rows, expected_rows)
except AssertionError:
    raise AssertionError("Variable 'rows' does not contain the correct values.")
finally:
    del expected_rows

10. Extract Alternating Rows from DataFrame¶

In [10]:

# try your code here
rows_every_other = ...

Solution:

In [ ]:

Assertions:

In [ ]:

11. Select specific columns¶

In [11]:

# try your code here
cols = ...

Solution:

In [ ]:

Assertions:

In [ ]:

12. Select first three columns¶

In [12]:

# try your code here
cols_first_three = ...

Solution:

In [18]:

cols_first_three = df.iloc[:, :3]

Assertions:

In [19]:

assert 'cols_first_three' in globals(), "Variable 'cols_first_three' does not exist."
assert isinstance(cols_first_three, pd.DataFrame), "Variable 'cols_first_three' is not a DataFrame."
expected_cols_first_three = df.iloc[:, :3]
try:
    pd.testing.assert_frame_equal(cols_first_three, expected_cols_first_three)
except AssertionError:
    raise AssertionError("Variable 'cols_first_three' does not contain the correct values.")
finally:
    del expected_cols_first_three

Statement of Completion#07bbfb13

Intro to Pandas for Data Analysis

Exploring DataFrames with Currency Data

Activities¶

1. Getting an Overview of the dataset¶

2. Select the correct ansewrs for dataset information¶

3. Get the statistics of the dataset¶

4. Count the number of unique currencies in the dataset¶

5. Identify the number of missing values in each column¶

6. Determine the highest currency number in the dataset¶

7. Select Currency Names¶

8. Get the details of the 3rd row¶

9. Select rows 10th to 15th (inclusive) from the DataFrame¶

10. Extract Alternating Rows from DataFrame¶

11. Select specific columns¶

12. Select first three columns¶

The End!¶

Activities¶

1. Getting an Overview of the dataset¶

2. Select the correct ansewrs for dataset information¶

3. Get the statistics of the dataset¶

4. Count the number of unique currencies in the dataset¶

5. Identify the number of missing values in each column¶

6. Determine the highest currency number in the dataset¶

7. Select Currency Names¶

8. Get the details of the 3rd row¶

9. Select rows 10 to 15 (inclusive) from the DataFrame¶

10. Extract Alternating Rows from DataFrame¶

11. Select specific columns¶

12. Select first three columns¶

The End!¶