5  Reading Data

5.1 Types of data - structured and unstructured

Reading data is the first step to extract information from it. Data can exist broadly in two formats:

  1. Structured data, and
  2. Unstructured data.

Structured data is typically stored in a tabular form, where rows in the data correspond to “observations” and columns correspond to “variables”. For example, the following dataset contains 5 observations, where each observation (or row) consists of information about a movie. The variables (or columns) contain different pieces of information about a given movie. As all variables for a given row are related to the same movie, the data below is also called relational data.

Title US Gross Production Budget Release Date Major Genre Creative Type Rotten Tomatoes Rating IMDB Rating
0 The Shawshank Redemption 28241469 25000000 Sep 23 1994 Drama Fiction 88 9.2
1 Inception 285630280 160000000 Jul 16 2010 Horror/Thriller Fiction 87 9.1
2 One Flew Over the Cuckoo's Nest 108981275 4400000 Nov 19 1975 Comedy Fiction 96 8.9
3 The Dark Knight 533345358 185000000 Jul 18 2008 Action/Adventure Fiction 93 8.9
4 Schindler's List 96067179 25000000 Dec 15 1993 Drama Non-Fiction 97 8.9

Unstructured data is data that is not organized in any pre-defined manner. Examples of unstructured data can be text files, audio/video files, images, Internet of Things (IoT) data, etc. Unstructured data is relatively harder to analyze as most of the analytical methods and tools are oriented towards structured data. However, an unstructured data can be used to obtain structured data, which in turn can be analyzed. For example, an image can be converted to an array of pixels - which will be structured data. Machine learning algorithms can then be used on the array to classify the image as that of a dog or a cat.

In this course, we will focus on analyzing structured data.

Pandas is a popular Python library used for working in tabular data (similar to the data stored in a spreadsheet). Pandas provides helper functions to read data from various file formats like CSV, Excel spreadsheets, HTML tables, JSON, SQL, and more.

5.2 Reading a csv file with Pandas

Structured data can be stored in a variety of formats. The most popular format is data_file_name.csv, where the extension csv stands for comma separated values. The variable values of each observation are separated by a comma in a .csv file. In other words, the delimiter is a comma in a csv file. However, the comma is not visible when a .csv file is opened with Microsoft Excel.

The below csv file contains day-wise Covid-19 data for Italy:

date,new_cases,new_deaths,new_tests
2020-04-21,2256.0,454.0,28095.0
2020-04-22,2729.0,534.0,44248.0
2020-04-23,3370.0,437.0,37083.0
2020-04-24,2646.0,464.0,95273.0
2020-04-25,3021.0,420.0,38676.0
2020-04-26,2357.0,415.0,24113.0
2020-04-27,2324.0,260.0,26678.0
2020-04-28,1739.0,333.0,37554.0
...

CSVs: A comma-separated values (CSV) file is a delimited text file that uses a comma to separate values. Each line of the file is a data record. Each record consists of one or more fields, separated by commas. A CSV file typically stores tabular data (numbers and text) in plain text, in which case each line will have the same number of fields. (Wikipedia)

First, let’s import the Pandas library. As a convention, it is imported with the alias pd.

import pandas as pd
import os

5.2.1 Using the read_csv function

The pd.read_csv function can be used to read a CSV file into a pandas DataFrame: a spreadsheet-like object for analyzing and processing data.

movie_ratings = pd.read_csv('./datasets/movie_ratings.csv')

The built-in python function type can be used to check the dataype of an object:

type(movie_ratings)
pandas.core.frame.DataFrame

We’ll learn more about DataFrame in a future lesson.

Note that I use the relative path to specify the file path for movie_ratings.csv, you may need to change it based on where you store the data file.

5.2.2 Data Overview

Once the data has been read, we may want to see what the data looks like. We’ll use another Pandas function head() to view the first few rows of the data.

movie_ratings.head()
Title US Gross Worldwide Gross Production Budget Release Date MPAA Rating Source Major Genre Creative Type IMDB Rating IMDB Votes
0 Opal Dreams 14443 14443 9000000 Nov 22 2006 PG/PG-13 Adapted screenplay Drama Fiction 6.5 468
1 Major Dundee 14873 14873 3800000 Apr 07 1965 PG/PG-13 Adapted screenplay Western/Musical Fiction 6.7 2588
2 The Informers 315000 315000 18000000 Apr 24 2009 R Adapted screenplay Horror/Thriller Fiction 5.2 7595
3 Buffalo Soldiers 353743 353743 15000000 Jul 25 2003 R Adapted screenplay Comedy Fiction 6.9 13510
4 The Last Sin Eater 388390 388390 2200000 Feb 09 2007 PG/PG-13 Adapted screenplay Drama Fiction 5.7 1012

Row Indices and column names (axis labels)

By default, when you create a pandas DataFrame (or Series) without specifying an index, pandas will automatically assign integer-based row indices starting from 0. These indices serve as the row labels and uniquely identify each row in the DataFrame. For example, the index 2 correponds to the row of the movie The Informers. By default, the indices are integers starting from 0. However, they can be changed (to even non-integer values) if desired by the user.

The bold text on top of the DataFrame refers to column names. For example, the column US Gross consists of the gross revenue of a movie in the US.

Collectively, the indices and column names are referred as axis labels.

Basic information We can view some basic information about the data frame using the .info method.

movie_ratings.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 2228 entries, 0 to 2227
Data columns (total 11 columns):
 #   Column             Non-Null Count  Dtype  
---  ------             --------------  -----  
 0   Title              2228 non-null   object 
 1   US Gross           2228 non-null   int64  
 2   Worldwide Gross    2228 non-null   int64  
 3   Production Budget  2228 non-null   int64  
 4   Release Date       2228 non-null   object 
 5   MPAA Rating        2228 non-null   object 
 6   Source             2228 non-null   object 
 7   Major Genre        2228 non-null   object 
 8   Creative Type      2228 non-null   object 
 9   IMDB Rating        2228 non-null   float64
 10  IMDB Votes         2228 non-null   int64  
dtypes: float64(1), int64(4), object(6)
memory usage: 191.6+ KB

The shape property of a pandas DataFrame provides a tuple that represents the dimensions of the DataFrame:

  • The first value in the tuple is the number of rows.
  • The second value in the tuple is the number of columns.
movie_ratings.shape
(2228, 11)

The columns property contains the list of columns within the data frame.

movie_ratings.columns
Index(['Title', 'US Gross', 'Worldwide Gross', 'Production Budget',
       'Release Date', 'MPAA Rating', 'Source', 'Major Genre', 'Creative Type',
       'IMDB Rating', 'IMDB Votes'],
      dtype='object')

You can view statistical information for numerical columns (mean, standard deviation, minimum/maximum values, and the number of non-empty values) using the .describe method.

movie_ratings.describe()
US Gross Worldwide Gross Production Budget IMDB Rating IMDB Votes
count 2.228000e+03 2.228000e+03 2.228000e+03 2228.000000 2228.000000
mean 5.076370e+07 1.019370e+08 3.816055e+07 6.239004 33585.154847
std 6.643081e+07 1.648589e+08 3.782604e+07 1.243285 47325.651561
min 0.000000e+00 8.840000e+02 2.180000e+02 1.400000 18.000000
25% 9.646188e+06 1.320737e+07 1.200000e+07 5.500000 6659.250000
50% 2.838649e+07 4.266892e+07 2.600000e+07 6.400000 18169.000000
75% 6.453140e+07 1.200000e+08 5.300000e+07 7.100000 40092.750000
max 7.601676e+08 2.767891e+09 3.000000e+08 9.200000 519541.000000

Functions & Methods we’ve looked so far

  • pd.read_csv - Read data from a CSV file into a Pandas DataFrame object
  • .info() - View basic infomation about rows, columns & data types
  • .shape - Get the number of rows & columns as a tuple
  • .columns - Get the list of column names
  • .describe() - View statistical information about numeric columns

5.3 Data Selection and Filtering

5.3.1 Extracting Column(s)

The first step when working with a DataFrame is often to extract one or more columns. To do this effectively, it’s helpful to understand the internal structure of a DataFrame. Conceptually, you can think of a DataFrame as a dictionary of lists, where the keys are column names, and the values are lists or arrays containing data for the respective columns.

# Pandas format is simliar to this
movie_ratings_dict = {
    'Title':  ['Opal Dreams', 'Major Dundee', 'The Informers', 'Buffalo Soldiers', 'The Last Sin Eater'],
    'US Gross':  [14443, 14873, 315000, 353743, 388390],
    'Worldwide Gross': [14443, 14873, 315000, 353743, 388390],
    'Production Budget': [9000000, 3800000, 18000000, 15000000, 2200000]
}

For dictionary, we use key to retrive its values

movie_ratings_dict['Title']
['Opal Dreams',
 'Major Dundee',
 'The Informers',
 'Buffalo Soldiers',
 'The Last Sin Eater']

Similar like dictionary, we can extract a column by its column name

movie_ratings['Title']
0                         Opal Dreams
1                        Major Dundee
2                       The Informers
3                    Buffalo Soldiers
4                  The Last Sin Eater
                    ...              
2223                      King Arthur
2224                            Mulan
2225                       Robin Hood
2226    Robin Hood: Prince of Thieves
2227                       Spiceworld
Name: Title, Length: 2228, dtype: object

Each column is a feature of the dataframe, we can also use. operator to extract a single column

movie_ratings.Title
0                         Opal Dreams
1                        Major Dundee
2                       The Informers
3                    Buffalo Soldiers
4                  The Last Sin Eater
                    ...              
2223                      King Arthur
2224                            Mulan
2225                       Robin Hood
2226    Robin Hood: Prince of Thieves
2227                       Spiceworld
Name: Title, Length: 2228, dtype: object

When extracting multiple columns, you need to place the column names inside a list.

movie_ratings[['Title', 'US Gross', 'Worldwide Gross' ]]
Title US Gross Worldwide Gross
0 Opal Dreams 14443 14443
1 Major Dundee 14873 14873
2 The Informers 315000 315000
3 Buffalo Soldiers 353743 353743
4 The Last Sin Eater 388390 388390
... ... ... ...
2223 King Arthur 51877963 203877963
2224 Mulan 120620254 303500000
2225 Robin Hood 105269730 310885538
2226 Robin Hood: Prince of Thieves 165493908 390500000
2227 Spiceworld 29342592 56042592

2228 rows × 3 columns

5.3.2 Extracting Row(s)

5.3.2.1 Extracting based on a Single Condition or Multiple Conditions

In many cases, we need to filter rows based on specific conditions or a combination of multiple conditions. Next, let’s explore how to use these conditions effectively to extract rows that meet our criteria, whether it’s a single condition or multiple conditions combined

# extracting the rows that have IMDB Rating greater than 8
movie_ratings[movie_ratings['IMDB Rating'] > 8]
Title US Gross Worldwide Gross Production Budget Release Date MPAA Rating Source Major Genre Creative Type IMDB Rating IMDB Votes
21 Gandhi, My Father 240425 1375194 5000000 Aug 03 2007 Other Adapted screenplay Drama Non-Fiction 8.1 50881
56 Ed Wood 5828466 5828466 18000000 Sep 30 1994 R Adapted screenplay Comedy Non-Fiction 8.1 74171
67 Requiem for a Dream 3635482 7390108 4500000 Oct 06 2000 Other Adapted screenplay Drama Fiction 8.5 185226
164 Trainspotting 16501785 24000785 3100000 Jul 19 1996 R Adapted screenplay Drama Fiction 8.2 150483
181 The Wizard of Oz 28202232 28202232 2777000 Aug 25 2039 G Adapted screenplay Western/Musical Fiction 8.3 102795
... ... ... ... ... ... ... ... ... ... ... ...
2090 Finding Nemo 339714978 867894287 94000000 May 30 2003 G Original Screenplay Action/Adventure Fiction 8.2 165006
2092 Toy Story 3 410640665 1046340665 200000000 Jun 18 2010 G Original Screenplay Action/Adventure Fiction 8.9 67380
2094 Avatar 760167650 2767891499 237000000 Dec 18 2009 PG/PG-13 Original Screenplay Action/Adventure Fiction 8.3 261439
2130 Scarface 44942821 44942821 25000000 Dec 09 1983 Other Adapted screenplay Drama Fiction 8.2 152262
2194 The Departed 133311000 290539042 90000000 Oct 06 2006 R Adapted screenplay Drama Fiction 8.5 264148

97 rows × 11 columns

To combine multiple conditions in pandas, you need to use the & (AND) and | (OR) operators. Make sure to enclose each condition in parentheses () for clarity and to ensure proper evaluation order.

# extracting the rows that have IMDB Rating greater than 8 and US Gross less than 1000000
movie_ratings[(movie_ratings['IMDB Rating'] > 8) & (movie_ratings['US Gross'] < 1000000)]
Title US Gross Worldwide Gross Production Budget Release Date MPAA Rating Source Major Genre Creative Type IMDB Rating IMDB Votes
21 Gandhi, My Father 240425 1375194 5000000 Aug 03 2007 Other Adapted screenplay Drama Non-Fiction 8.1 50881
636 Lake of Fire 25317 25317 6000000 Oct 03 2007 Other Adapted screenplay Documentary Non-Fiction 8.4 1027

5.3.2.2 Negating Conditions with the ~ Operator

The tilde (~) is used for negating boolean conditions in pandas, making it a useful tool for excluding specific values or rows.

In some cases, we may want to extract rows that do not meet a specific condition. To accomplish this, we can use the ~ operator, which negates a condition. This operator allows us to filter data by excluding rows that satisfy a particular condition, making it a powerful tool for refining our queries. Let’s explore how to use the ~ operator to negate conditions and select rows that do not meet our criteria

# Excluding the rows that have IMDB Rating that equals 8 using the tilde ~
movie_ratings[~(movie_ratings['IMDB Rating'] == 8)]
Title US Gross Worldwide Gross Production Budget Release Date MPAA Rating Source Major Genre Creative Type IMDB Rating IMDB Votes
0 Opal Dreams 14443 14443 9000000 Nov 22 2006 PG/PG-13 Adapted screenplay Drama Fiction 6.5 468
1 Major Dundee 14873 14873 3800000 Apr 07 1965 PG/PG-13 Adapted screenplay Western/Musical Fiction 6.7 2588
2 The Informers 315000 315000 18000000 Apr 24 2009 R Adapted screenplay Horror/Thriller Fiction 5.2 7595
3 Buffalo Soldiers 353743 353743 15000000 Jul 25 2003 R Adapted screenplay Comedy Fiction 6.9 13510
4 The Last Sin Eater 388390 388390 2200000 Feb 09 2007 PG/PG-13 Adapted screenplay Drama Fiction 5.7 1012
... ... ... ... ... ... ... ... ... ... ... ...
2223 King Arthur 51877963 203877963 90000000 Jul 07 2004 PG/PG-13 Adapted screenplay Action/Adventure Fiction 6.2 53106
2224 Mulan 120620254 303500000 90000000 Jun 19 1998 G Adapted screenplay Action/Adventure Non-Fiction 7.2 34256
2225 Robin Hood 105269730 310885538 210000000 May 14 2010 PG/PG-13 Adapted screenplay Action/Adventure Fiction 6.9 34501
2226 Robin Hood: Prince of Thieves 165493908 390500000 50000000 Jun 14 1991 PG/PG-13 Adapted screenplay Action/Adventure Fiction 6.7 54480
2227 Spiceworld 29342592 56042592 25000000 Jan 23 1998 PG/PG-13 Adapted screenplay Comedy Fiction 2.9 18010

2191 rows × 11 columns

Another way to exclude rows where a column equals a certain value, you can use != to create the condition.

# using the != operator
movie_ratings[movie_ratings['IMDB Rating'] != 8]
Title US Gross Worldwide Gross Production Budget Release Date MPAA Rating Source Major Genre Creative Type IMDB Rating IMDB Votes
0 Opal Dreams 14443 14443 9000000 Nov 22 2006 PG/PG-13 Adapted screenplay Drama Fiction 6.5 468
1 Major Dundee 14873 14873 3800000 Apr 07 1965 PG/PG-13 Adapted screenplay Western/Musical Fiction 6.7 2588
2 The Informers 315000 315000 18000000 Apr 24 2009 R Adapted screenplay Horror/Thriller Fiction 5.2 7595
3 Buffalo Soldiers 353743 353743 15000000 Jul 25 2003 R Adapted screenplay Comedy Fiction 6.9 13510
4 The Last Sin Eater 388390 388390 2200000 Feb 09 2007 PG/PG-13 Adapted screenplay Drama Fiction 5.7 1012
... ... ... ... ... ... ... ... ... ... ... ...
2223 King Arthur 51877963 203877963 90000000 Jul 07 2004 PG/PG-13 Adapted screenplay Action/Adventure Fiction 6.2 53106
2224 Mulan 120620254 303500000 90000000 Jun 19 1998 G Adapted screenplay Action/Adventure Non-Fiction 7.2 34256
2225 Robin Hood 105269730 310885538 210000000 May 14 2010 PG/PG-13 Adapted screenplay Action/Adventure Fiction 6.9 34501
2226 Robin Hood: Prince of Thieves 165493908 390500000 50000000 Jun 14 1991 PG/PG-13 Adapted screenplay Action/Adventure Fiction 6.7 54480
2227 Spiceworld 29342592 56042592 25000000 Jan 23 1998 PG/PG-13 Adapted screenplay Comedy Fiction 2.9 18010

2191 rows × 11 columns

5.3.3 Extracting Subsets of Rows and Columns

Sometimes we may be interested in working with a subset of rows and columns of the data, instead of working with the entire dataset. The indexing operators loc and iloc provide a convenient way of selecting a subset of desired rows and columns.

Let us first sort the movie_ratings data frame by IMDB Rating.

movie_ratings_sorted = movie_ratings.sort_values(by = 'IMDB Rating', ascending = False)
movie_ratings_sorted.head()
Title US Gross Worldwide Gross Production Budget Release Date MPAA Rating Source Major Genre Creative Type IMDB Rating IMDB Votes
182 The Shawshank Redemption 28241469 28241469 25000000 Sep 23 1994 R Adapted screenplay Drama Fiction 9.2 519541
2084 Inception 285630280 753830280 160000000 Jul 16 2010 PG/PG-13 Original Screenplay Horror/Thriller Fiction 9.1 188247
790 Schindler's List 96067179 321200000 25000000 Dec 15 1993 R Adapted screenplay Drama Non-Fiction 8.9 276283
1962 Pulp Fiction 107928762 212928762 8000000 Oct 14 1994 R Original Screenplay Drama Fiction 8.9 417703
561 The Dark Knight 533345358 1022345358 185000000 Jul 18 2008 PG/PG-13 Adapted screenplay Action/Adventure Fiction 8.9 465000

5.3.3.1 Subsetting the DataFrame by loc

The operator loc uses axis labels (row indices and column names) to subset the data.

Let’s subset the title, worldwide gross, production budget, and IMDB raring of top 3 movies.

# Subsetting the DataFrame by loc - using axis labels
movies_subset = movie_ratings_sorted.loc[[182,2084, 2092],[ 'Title', 'IMDB Rating', 'US Gross', 'Worldwide Gross', 'Production Budget']]
movies_subset
Title IMDB Rating US Gross Worldwide Gross Production Budget
182 The Shawshank Redemption 9.2 28241469 28241469 25000000
2084 Inception 9.1 285630280 753830280 160000000
2092 Toy Story 3 8.9 410640665 1046340665 200000000

The : symbol in .loc is a slicing operator that represents a range or all elements in the specified dimension (rows or columns). Use : alone to select all rows/columns, or with start/end points to slice specific parts of the DataFrame.

# Subsetting the DataFrame by loc - using axis labels. the colon is used to select all rows
movies_subset = movie_ratings_sorted.loc[:,['Title','Worldwide Gross','Production Budget','IMDB Rating']]
movies_subset
Title Worldwide Gross Production Budget IMDB Rating
182 The Shawshank Redemption 28241469 25000000 9.2
2084 Inception 753830280 160000000 9.1
790 Schindler's List 321200000 25000000 8.9
1962 Pulp Fiction 212928762 8000000 8.9
561 The Dark Knight 1022345358 185000000 8.9
... ... ... ... ...
1051 Glitter 4273372 8500000 2.0
1495 Disaster Movie 34690901 20000000 1.7
1116 Crossover 7009668 5600000 1.7
805 From Justin to Kelly 4922166 12000000 1.6
1147 Super Babies: Baby Geniuses 2 9109322 20000000 1.4

2228 rows × 4 columns

# Subsetting the DataFrame by loc - using axis labels. the colon is used to select a range of rows
movies_subset = movie_ratings_sorted.loc[182:561,['Title','Worldwide Gross','Production Budget','IMDB Rating']]
movies_subset
Title Worldwide Gross Production Budget IMDB Rating
182 The Shawshank Redemption 28241469 25000000 9.2
2084 Inception 753830280 160000000 9.1
790 Schindler's List 321200000 25000000 8.9
1962 Pulp Fiction 212928762 8000000 8.9
561 The Dark Knight 1022345358 185000000 8.9

Combining .loc with condition(s) to extract specific rows and columns based on criteria

# extracting the rows that have IMDB Rating greater than 8 or US Gross less than 1000000, only extract the Title and IMDB Rating columns
movie_ratings[(movie_ratings['IMDB Rating'] > 8) & (movie_ratings['US Gross'] < 1000000)][['Title','IMDB Rating']]

#using loc to extract the rows that have IMDB Rating greater than 8 or US Gross less than 1000000, only extract the Title and IMDB Rating columns
movie_ratings.loc[(movie_ratings['IMDB Rating'] > 8) & (movie_ratings['US Gross'] < 1000000),['Title','IMDB Rating']]
Title IMDB Rating
21 Gandhi, My Father 8.1
636 Lake of Fire 8.4

5.3.3.2 Subsetting the DataFrame by iloc

while iloc uses the position of rows or columns, where position has values 0,1,2,3,…and so on, for rows from top to bottom and columns from left to right. In other words, the first row has position 0, the second row has position 1, the third row has position 2, and so on. Similarly, the first column from left has position 0, the second column from left has position 1, the third column from left has position 2, and so on.

# let's check  the movie_ratings_sorted DataFrame
movie_ratings_sorted.head()
Title US Gross Worldwide Gross Production Budget Release Date MPAA Rating Source Major Genre Creative Type IMDB Rating IMDB Votes
182 The Shawshank Redemption 28241469 28241469 25000000 Sep 23 1994 R Adapted screenplay Drama Fiction 9.2 519541
2084 Inception 285630280 753830280 160000000 Jul 16 2010 PG/PG-13 Original Screenplay Horror/Thriller Fiction 9.1 188247
790 Schindler's List 96067179 321200000 25000000 Dec 15 1993 R Adapted screenplay Drama Non-Fiction 8.9 276283
1962 Pulp Fiction 107928762 212928762 8000000 Oct 14 1994 R Original Screenplay Drama Fiction 8.9 417703
561 The Dark Knight 533345358 1022345358 185000000 Jul 18 2008 PG/PG-13 Adapted screenplay Action/Adventure Fiction 8.9 465000

After sorting, the position-based index changes, while the label-based index remains unchanged. Let’s pass the position-based index to iloc to retrieve the top 2 rows from the movie_ratings_sorted DataFrame.

movie_ratings_sorted.iloc[0:2,:]
Title US Gross Worldwide Gross Production Budget Release Date MPAA Rating Source Major Genre Creative Type IMDB Rating IMDB Votes
182 The Shawshank Redemption 28241469 28241469 25000000 Sep 23 1994 R Adapted screenplay Drama Fiction 9.2 519541
2084 Inception 285630280 753830280 160000000 Jul 16 2010 PG/PG-13 Original Screenplay Horror/Thriller Fiction 9.1 188247

It is important to note that the endpoint is excluded in an iloc slice.

For comparison, let’s pass the same argument to loc and see what it returns.

movie_ratings_sorted.loc[0:2,:]
Title US Gross Worldwide Gross Production Budget Release Date MPAA Rating Source Major Genre Creative Type IMDB Rating IMDB Votes
0 Opal Dreams 14443 14443 9000000 Nov 22 2006 PG/PG-13 Adapted screenplay Drama Fiction 6.5 468
851 Star Trek: Generations 75671262 120000000 38000000 Nov 18 1994 PG/PG-13 Adapted screenplay Action/Adventure Fiction 6.5 26465
140 Tuck Everlasting 19161999 19344615 15000000 Oct 11 2002 PG/PG-13 Adapted screenplay Drama Fiction 6.5 6639
708 De-Lovely 13337299 18396382 4000000 Jun 25 2004 PG/PG-13 Adapted screenplay Drama Fiction 6.5 6086
705 Flyboys 13090630 14816379 60000000 Sep 22 2006 PG/PG-13 Adapted screenplay Drama Non-Fiction 6.5 13934
... ... ... ... ... ... ... ... ... ... ... ...
955 The Brothers Solomon 900926 900926 10000000 Sep 07 2007 R Original Screenplay Comedy Fiction 5.2 6044
1637 Drumline 56398162 56398162 20000000 Dec 13 2002 PG/PG-13 Original Screenplay Comedy Fiction 5.2 18165
1610 Hollywood Homicide 30207785 51107785 75000000 Jun 13 2003 PG/PG-13 Original Screenplay Action/Adventure Fiction 5.2 16452
569 Doom 28212337 54612337 70000000 Oct 21 2005 R Adapted screenplay Horror/Thriller Fiction 5.2 39473
2 The Informers 315000 315000 18000000 Apr 24 2009 R Adapted screenplay Horror/Thriller Fiction 5.2 7595

812 rows × 11 columns

As you can see, : is used in the same way as in loc to denote a range of the index. All rows between label indices 0 and 2, inclusive of both ends, are returned.

# Subsetting the DataFrame by iloc - using index of the position of rows and columns
movies_iloc_subset1 = movie_ratings_sorted.iloc[0:10,[0,2,3,9]]
movies_iloc_subset1
Title Worldwide Gross Production Budget IMDB Rating
182 The Shawshank Redemption 28241469 25000000 9.2
2084 Inception 753830280 160000000 9.1
2092 Toy Story 3 1046340665 200000000 8.9
1962 Pulp Fiction 212928762 8000000 8.9
790 Schindler's List 321200000 25000000 8.9
561 The Dark Knight 1022345358 185000000 8.9
184 Cidade de Deus 28763397 3300000 8.8
487 The Lord of the Rings: The Fellowship of the Ring 868621686 109000000 8.8
497 The Lord of the Rings: The Return of the King 1133027325 94000000 8.8
1081 C'era una volta il West 5321508 5000000 8.8

Can you use .iloc for conditional filtering, why or why not?

To recap, here are the Key differences betweenloc and iloc in pandas:

  • Indexing Type:

    • loc uses labels (names) for indexing.
    • iloc uses integer positions for indexing.

  • Inclusion of Endpoints:

    • In a loc slice, both endpoints are included.
    • In an iloc slice, the endpoint is excluded.

5.3.4 Finding minimum/maximum of a column

When working with pandas, there are two main options for locating the minimum or maximum values in a DataFrame column:

  • idxmin() and idxmax(): return the index label of the first occurrence of the maximum or minimum value in a specified column.
# movie_ratings_sorted.iloc[position_max_wgross,:]
max_index = movie_ratings_sorted['Worldwide Gross'].idxmax()
min_index = movie_ratings_sorted['Worldwide Gross'].idxmin()
print("Max index: ", max_index)
print("Min index: ", min_index)
Max index:  2094
Min index:  896

idxmin() and idxmax() return the index label of the minimum or maximum value in a column. You can use these returned index labels with .loc to extract the corresponding row.

print(movie_ratings_sorted.loc[max_index,'Worldwide Gross'])
print(movie_ratings_sorted.loc[min_index,'Worldwide Gross'])
2767891499
884
  • argmax() and argmin(): Return the integer position of the first occurrence of the maximum or minimum value in a column. You can use these integer positions with .iloc to extract the corresponding row
# using argmax and argmin, which return the index of the maximum and minimum values
max_position = movie_ratings_sorted['Worldwide Gross'].argmax()
min_position = movie_ratings_sorted['Worldwide Gross'].argmin()
print("max position:", max_position)
print("min position:", min_position)

# using iloc to get the row with the maximum and minimum values
print(movie_ratings_sorted.iloc[max_position, 2])
print(movie_ratings_sorted.iloc[min_position, 2])
max position: 48
min position: 2149
2767891499
884

Tips:

  • When working with non-unique or custom indices, it is recommended to use idxmax() and idxmin() to retrieve index labels, as argmax() may be less intuitive in such scenarios.
  • For DataFrames, use .idxmax(axis=1) or .idxmin(axis=1) to find the index labels corresponding to the maximum or minimum values across rows, rather than columns.

5.3.5 Finding the top n minimum/maximum values of a column

To find the top n minimum or maximum values of a column in a pandas DataFrame, you can use the nsmallest() and nlargest() methods. Here’s how you can do it:

# find the top 3 movies with the highest worldwide gross
top_3_movies = movie_ratings.nlargest(3, 'Worldwide Gross')
top_3_movies
Title US Gross Worldwide Gross Production Budget Release Date MPAA Rating Source Major Genre Creative Type IMDB Rating IMDB Votes
2094 Avatar 760167650 2767891499 237000000 Dec 18 2009 PG/PG-13 Original Screenplay Action/Adventure Fiction 8.3 261439
2093 Titanic 600788188 1842879955 200000000 Dec 19 1997 PG/PG-13 Original Screenplay Horror/Thriller Fiction 7.4 240732
497 The Lord of the Rings: The Return of the King 377027325 1133027325 94000000 Dec 17 2003 PG/PG-13 Adapted screenplay Action/Adventure Fiction 8.8 364077

Let’s double check the result using the movie_ratings_sorted dataframe

movie_ratings_sorted['Worldwide Gross'].nlargest(3)
2094    2767891499
2093    1842879955
497     1133027325
Name: Worldwide Gross, dtype: int64

Let’s find the 3 movies with the smallest IMDb votes using the nsmallest method

bottom_3_movies = movie_ratings.nsmallest(3, 'IMDB Votes')
bottom_3_movies
Title US Gross Worldwide Gross Production Budget Release Date MPAA Rating Source Major Genre Creative Type IMDB Rating IMDB Votes
1000 Teeth 347578 2300349 2000000 Jan 18 2008 R Original Screenplay Comedy Fiction 5.5 18
1261 Birth 5005899 14603001 20000000 Oct 29 2004 R Original Screenplay Drama Fiction 6.3 25
1298 CachÈ 3647381 17147381 8000000 Dec 23 2005 R Original Screenplay Drama Fiction 5.5 26

Let’s check the result using sort_values method

# using the sort_values method
movie_ratings_sorted['IMDB Votes'].nsmallest(3)
1000    18
1261    25
1298    26
Name: IMDB Votes, dtype: int64

5.4 Writing data to a .csv file

The Pandas function to_csv can be used to write (or export) data to a csv. Below is an example.

#Exporting the data of the top 250 movies to a csv file
movie_ratings.to_csv('../data/movie_rating_exported.csv')
# check if the file has been exported
os.listdir('../data')
['bestseller_books.txt',
 'country-capital-lat-long-population.csv',
 'covid.csv',
 'fifa_data.csv',
 'food_quantity.csv',
 'gas_prices.csv',
 'gdp_lifeExpectancy.csv',
 'LOTR 2.csv',
 'LOTR.csv',
 'movies.csv',
 'movies_cleaned.csv',
 'movie_ratings.csv',
 'movie_ratings.txt',
 'movie_rating_exported.csv',
 'party_nyc.csv',
 'price.csv',
 'question_json_data.json',
 'spotify_data.csv',
 'stocks.csv']

5.5 Reading other data formats - txt, html, json

Although .csv is a very popular format for structured data, data is found in several other formats as well. Some of the other data formats are .txt, .html and .json.

5.5.1 Reading .txt files

The txt format offers some additional flexibility as compared to the csv format. In the csv format, the delimiter is a comma (or the column values are separated by a comma). However, in a txt file, the delimiter can be anything as desired by the user. Let us read the file movie_ratings.txt, where the variable values are separated by a tab character.

movie_ratings_txt = pd.read_csv('../data/movie_ratings.txt',sep='\t')
movie_ratings_txt.head()
Unnamed: 0 Title US Gross Worldwide Gross Production Budget Release Date MPAA Rating Source Major Genre Creative Type IMDB Rating IMDB Votes
0 0 Opal Dreams 14443 14443 9000000 Nov 22 2006 PG/PG-13 Adapted screenplay Drama Fiction 6.5 468
1 1 Major Dundee 14873 14873 3800000 Apr 07 1965 PG/PG-13 Adapted screenplay Western/Musical Fiction 6.7 2588
2 2 The Informers 315000 315000 18000000 Apr 24 2009 R Adapted screenplay Horror/Thriller Fiction 5.2 7595
3 3 Buffalo Soldiers 353743 353743 15000000 Jul 25 2003 R Adapted screenplay Comedy Fiction 6.9 13510
4 4 The Last Sin Eater 388390 388390 2200000 Feb 09 2007 PG/PG-13 Adapted screenplay Drama Fiction 5.7 1012

We use the function read_csv to read a txt file. However, we mention the tab character (r”) as a separator of variable values.

Note that there is no need to remember the argument name - sep for specifying the delimiter. You can always refer to the read_csv() documentation to find the relevant argument.

5.5.2 Reading HTML data

The Pandas function read_html searches for tabular data, i.e., data contained within the <table> tags of an html file. Let us read the tables in the GDP per capita page on Wikipedia.

#Reading all the tables from the Wikipedia page on GDP per capita
tables = pd.read_html('https://en.wikipedia.org/wiki/List_of_countries_by_GDP_(nominal)_per_capita')

All the tables will be read and stored in the variable named as tables. Let us find the datatype of the variable tables.

#Finidng datatype of the variable - tables
type(tables)
list

The variable - tables is a list of all the tables read from the HTML data.

#Number of tables read from the page
len(tables)
6

The in-built function len can be used to find the length of the list - tables or the number of tables read from the Wikipedia page. Let us check out the first table.

#Checking out the first table. Note that the index of the first table will be 0.
tables[0]
0 1 2
0 >$60,000 $50,000–$60,000 $40,000–$50,000 $30,0... $20,000–$30,000 $10,000–$20,000 $5,000–$10,000... $1,000–$2,500 $500–$1,000 <$500 No data

The above table doesn’t seem to be useful. Let us check out the second table.

#Checking out the second table. Note that the index of the first table will be 1.
tables[1]
Country/Territory IMF[4][5] World Bank[6] United Nations[7]
Country/Territory Estimate Year Estimate Year Estimate Year
0 Monaco — — 240862 2022 234317 2021
1 Liechtenstein — — 187267 2022 169260 2021
2 Luxembourg 131384 2024 128259 2023 133745 2021
3 Bermuda — — 123091 2022 112653 2021
4 Ireland 106059 2024 103685 2023 101109 2021
... ... ... ... ... ... ... ...
218 Malawi 481 2024 673 2023 613 2021
219 South Sudan 422 2024 1072 2015 400 2021
220 Afghanistan 422 2022 353 2022 373 2021
221 Syria — — 421 2021 925 2021
222 Burundi 230 2024 200 2023 311 2021

223 rows × 7 columns

The above table contains the estimated GDP per capita of all countries. This is the table that is likely to be relevant to a user interested in analyzing GDP per capita of countries. Instead of reading all tables of an HTML file, we can focus the search to tables containing certain relevant keywords. Let us try searching all table containing the word ‘Country’.

#Reading all the tables from the Wikipedia page on GDP per capita, containing the word 'Country'
tables = pd.read_html('https://en.wikipedia.org/wiki/List_of_countries_by_GDP_(nominal)_per_capita', match = 'Country')

The match argument can be used to specify the keywords to be present in the table to be read.

len(tables)
1

Only one table contains the keyword - ‘Country’. Let us check out the table obtained.

#Table having the keyword - 'Country' from the HTML page
tables[0]
Country/Territory IMF[4][5] World Bank[6] United Nations[7]
Country/Territory Estimate Year Estimate Year Estimate Year
0 Monaco — — 240862 2022 234317 2021
1 Liechtenstein — — 187267 2022 169260 2021
2 Luxembourg 131384 2024 128259 2023 133745 2021
3 Bermuda — — 123091 2022 112653 2021
4 Ireland 106059 2024 103685 2023 101109 2021
... ... ... ... ... ... ... ...
218 Malawi 481 2024 673 2023 613 2021
219 South Sudan 422 2024 1072 2015 400 2021
220 Afghanistan 422 2022 353 2022 373 2021
221 Syria — — 421 2021 925 2021
222 Burundi 230 2024 200 2023 311 2021

223 rows × 7 columns

The argument match helps with a more focussed search, and helps us discard irrelevant tables.

5.5.3 Reading JSON data

JSON stands for JavaScript Object Notation, in which the data is stored and transmitted as plain text. A couple of benefits of the JSON format are:

  1. Since the format is text only, JSON data can easily be exchanged between web applications, and used by any programming language.

  2. Unlike the csv format, JSON supports a hierarchical data structure, and is easier to integrate with APIs.

The JSON format can support a hierachical data structure, as it is built on the following two data structures (Source: technical documentation):

  • A collection of name/value pairs. In various languages, this is realized as an object, record, struct, dictionary, hash table, keyed list, or associative array.
  • An ordered list of values. In most languages, this is realized as an array, vector, list, or sequence.

These are universal data structures. Virtually all modern programming languages support them in one form or another. It makes sense that a data format that is interchangeable with programming languages also be based on these structures.

The Pandas function read_json converts a JSON string to a Pandas DataFrame. The function dumps() of the json library converts a Python object to a JSON string.

Lets read the JSON data on Ted Talks.

tedtalks_data = pd.read_json('https://raw.githubusercontent.com/cwkenwaysun/TEDmap/master/data/TED_Talks.json')
tedtalks_data.head()
id speaker headline URL description transcript_URL month_filmed year_filmed event duration date_published tags newURL date views rates
0 7 David Pogue Simplicity sells http://www.ted.com/talks/view/id/7 New York Times columnist David Pogue takes aim... http://www.ted.com/talks/view/id/7/transcript?... 2 2006 TED2006 0:21:26 6/27/06 simplicity,computers,software,interface design... https://www.ted.com/talks/david_pogue_says_sim... 2006-06-27 1646773 [{'id': 7, 'name': 'Funny', 'count': 968}, {'i...
1 6 Craig Venter Sampling the ocean's DNA http://www.ted.com/talks/view/id/6 Genomics pioneer Craig Venter takes a break fr... http://www.ted.com/talks/view/id/6/transcript?... 7 2005 TEDGlobal 2005 0:16:51 2004/05/07 biotech,invention,oceans,genetics,DNA,biology,... https://www.ted.com/talks/craig_venter_on_dna_... 2004-05-07 562625 [{'id': 3, 'name': 'Courageous', 'count': 21},...
2 4 Burt Rutan The real future of space exploration http://www.ted.com/talks/view/id/4 In this passionate talk, legendary spacecraft ... http://www.ted.com/talks/view/id/4/transcript?... 2 2006 TED2006 0:19:37 10/25/06 aircraft,flight,industrial design,NASA,rocket ... https://www.ted.com/talks/burt_rutan_sees_the_... 2006-10-25 2046869 [{'id': 3, 'name': 'Courageous', 'count': 169}...
3 3 Ashraf Ghani How to rebuild a broken state http://www.ted.com/talks/view/id/3 Ashraf Ghani's passionate and powerful 10-minu... http://www.ted.com/talks/view/id/3/transcript?... 7 2005 TEDGlobal 2005 0:18:45 10/18/06 corruption,poverty,economics,investment,milita... https://www.ted.com/talks/ashraf_ghani_on_rebu... 2006-10-18 814554 [{'id': 3, 'name': 'Courageous', 'count': 140}...
4 5 Chris Bangle Great cars are great art http://www.ted.com/talks/view/id/5 American designer Chris Bangle explains his ph... http://www.ted.com/talks/view/id/5/transcript?... 2 2002 TED2002 0:20:04 2004/05/07 cars,industrial design,transportation,inventio... https://www.ted.com/talks/chris_bangle_says_gr... 2004-05-07 870950 [{'id': 1, 'name': 'Beautiful', 'count': 89}, ... 
[{'question': "What is the data type of values in the last column (named 'rates') of the above dataset on ted talks",
  'type': 'multiple_choice',
  'answers': [{'answer': 'list', 'correct': True, 'feedback': 'Correct!'},
   {'answer': 'string',
    'correct': False,
    'feedback': 'Incorrect. Use the type function on the variable to find its datatype.'},
   {'answer': 'numeric',
    'correct': False,
    'feedback': 'Incorrect. Use the type function on the variable to find its datatype.'},
   {'answer': 'dictionary',
    'correct': False,
    'feedback': 'Incorrect. Use the type function on the variable to find its datatype.'}]}]

This JSON data contains nested structures, such as lists and dictionaries, which require a deeper understanding to effectively structure. We will address this in future lectures

5.5.4 Reading data from a URL in Python

This process typically involves using the requests library, which allows you to send HTTP requests and handle responses easily.

You’ll need to install it using pip

We’ll use the CoinGecko API, which provides cryptocurrency market data. Here’s an example of how to retrieve current market data:

import requests

# Define the URL of the API
url = 'https://api.coingecko.com/api/v3/coins/markets?vs_currency=usd'

# Send a GET request to the URL
response = requests.get(url)

# Check if the request was successful
if response.status_code == 200:
    # Parse the JSON data
    data = response.json()
    print(data)
else:
    print(f"Failed to retrieve data: {response.status_code}")
[{'id': 'bitcoin', 'symbol': 'btc', 'name': 'Bitcoin', 'image': 'https://coin-images.coingecko.com/coins/images/1/large/bitcoin.png?1696501400', 'current_price': 62490, 'market_cap': 1235032675967, 'market_cap_rank': 1, 'fully_diluted_valuation': 1312231173412, 'total_volume': 34554624888, 'high_24h': 64500, 'low_24h': 62100, 'price_change_24h': -1170.466984852057, 'price_change_percentage_24h': -1.83862, 'market_cap_change_24h': -23150749922.800293, 'market_cap_change_percentage_24h': -1.84001, 'circulating_supply': 19764571.0, 'total_supply': 21000000.0, 'max_supply': 21000000.0, 'ath': 73738, 'ath_change_percentage': -15.21931, 'ath_date': '2024-03-14T07:10:36.635Z', 'atl': 67.81, 'atl_change_percentage': 92093.56211, 'atl_date': '2013-07-06T00:00:00.000Z', 'roi': None, 'last_updated': '2024-10-08T00:54:10.271Z'}, {'id': 'ethereum', 'symbol': 'eth', 'name': 'Ethereum', 'image': 'https://coin-images.coingecko.com/coins/images/279/large/ethereum.png?1696501628', 'current_price': 2434.1, 'market_cap': 293023922868, 'market_cap_rank': 2, 'fully_diluted_valuation': 293018397207, 'total_volume': 16968707061, 'high_24h': 2518.07, 'low_24h': 2405.58, 'price_change_24h': -53.64323369453086, 'price_change_percentage_24h': -2.1563, 'market_cap_change_24h': -6417354167.597656, 'market_cap_change_percentage_24h': -2.14311, 'circulating_supply': 120379113.24004, 'total_supply': 120376843.206498, 'max_supply': None, 'ath': 4878.26, 'ath_change_percentage': -50.02647, 'ath_date': '2021-11-10T14:24:19.604Z', 'atl': 0.432979, 'atl_change_percentage': 562938.84293, 'atl_date': '2015-10-20T00:00:00.000Z', 'roi': {'times': 51.08601756823208, 'currency': 'btc', 'percentage': 5108.601756823207}, 'last_updated': '2024-10-08T00:54:07.565Z'}, {'id': 'tether', 'symbol': 'usdt', 'name': 'Tether', 'image': 'https://coin-images.coingecko.com/coins/images/325/large/Tether.png?1696501661', 'current_price': 0.999711, 'market_cap': 119827577712, 'market_cap_rank': 3, 'fully_diluted_valuation': 119827577712, 'total_volume': 55698442555, 'high_24h': 1.004, 'low_24h': 0.99633, 'price_change_24h': -0.001198196616044811, 'price_change_percentage_24h': -0.11971, 'market_cap_change_24h': -23449752.259277344, 'market_cap_change_percentage_24h': -0.01957, 'circulating_supply': 119857245003.333, 'total_supply': 119857245003.333, 'max_supply': None, 'ath': 1.32, 'ath_change_percentage': -24.39102, 'ath_date': '2018-07-24T00:00:00.000Z', 'atl': 0.572521, 'atl_change_percentage': 74.73226, 'atl_date': '2015-03-02T00:00:00.000Z', 'roi': None, 'last_updated': '2024-10-08T00:54:12.212Z'}, {'id': 'binancecoin', 'symbol': 'bnb', 'name': 'BNB', 'image': 'https://coin-images.coingecko.com/coins/images/825/large/bnb-icon2_2x.png?1696501970', 'current_price': 568.59, 'market_cap': 82947258162, 'market_cap_rank': 4, 'fully_diluted_valuation': 82947258162, 'total_volume': 799134450, 'high_24h': 582.45, 'low_24h': 563.77, 'price_change_24h': -7.260884048894809, 'price_change_percentage_24h': -1.2609, 'market_cap_change_24h': -1068798750.590683, 'market_cap_change_percentage_24h': -1.27214, 'circulating_supply': 145887575.79, 'total_supply': 145887575.79, 'max_supply': 200000000.0, 'ath': 717.48, 'ath_change_percentage': -20.80519, 'ath_date': '2024-06-06T14:10:59.816Z', 'atl': 0.0398177, 'atl_change_percentage': 1426911.78115, 'atl_date': '2017-10-19T00:00:00.000Z', 'roi': None, 'last_updated': '2024-10-08T00:54:13.445Z'}, {'id': 'solana', 'symbol': 'sol', 'name': 'Solana', 'image': 'https://coin-images.coingecko.com/coins/images/4128/large/solana.png?1718769756', 'current_price': 144.62, 'market_cap': 67873121370, 'market_cap_rank': 5, 'fully_diluted_valuation': 84775812388, 'total_volume': 3237160849, 'high_24h': 151.52, 'low_24h': 143.59, 'price_change_24h': -4.597486919219733, 'price_change_percentage_24h': -3.081, 'market_cap_change_24h': -2171285003.411751, 'market_cap_change_percentage_24h': -3.09987, 'circulating_supply': 469265730.374483, 'total_supply': 586128687.109744, 'max_supply': None, 'ath': 259.96, 'ath_change_percentage': -44.35227, 'ath_date': '2021-11-06T21:54:35.825Z', 'atl': 0.500801, 'atl_change_percentage': 28785.99929, 'atl_date': '2020-05-11T19:35:23.449Z', 'roi': None, 'last_updated': '2024-10-08T00:54:11.774Z'}, {'id': 'usd-coin', 'symbol': 'usdc', 'name': 'USDC', 'image': 'https://coin-images.coingecko.com/coins/images/6319/large/usdc.png?1696506694', 'current_price': 0.99982, 'market_cap': 35336575331, 'market_cap_rank': 6, 'fully_diluted_valuation': 35336582362, 'total_volume': 7937781611, 'high_24h': 1.003, 'low_24h': 0.996693, 'price_change_24h': -0.000988042958259383, 'price_change_percentage_24h': -0.09872, 'market_cap_change_24h': -295654890.59819794, 'market_cap_change_percentage_24h': -0.82974, 'circulating_supply': 35344977832.893, 'total_supply': 35344984865.7762, 'max_supply': None, 'ath': 1.17, 'ath_change_percentage': -14.68676, 'ath_date': '2019-05-08T00:40:28.300Z', 'atl': 0.877647, 'atl_change_percentage': 13.99504, 'atl_date': '2023-03-11T08:02:13.981Z', 'roi': None, 'last_updated': '2024-10-08T00:54:12.579Z'}, {'id': 'ripple', 'symbol': 'xrp', 'name': 'XRP', 'image': 'https://coin-images.coingecko.com/coins/images/44/large/xrp-symbol-white-128.png?1696501442', 'current_price': 0.531761, 'market_cap': 30080966724, 'market_cap_rank': 7, 'fully_diluted_valuation': 53173544466, 'total_volume': 1488925609, 'high_24h': 0.54488, 'low_24h': 0.529092, 'price_change_24h': -0.006764730959881505, 'price_change_percentage_24h': -1.25616, 'market_cap_change_24h': -380244778.19135284, 'market_cap_change_percentage_24h': -1.24829, 'circulating_supply': 56564039920.0, 'total_supply': 99987161962.0, 'max_supply': 100000000000.0, 'ath': 3.4, 'ath_change_percentage': -84.34345, 'ath_date': '2018-01-07T00:00:00.000Z', 'atl': 0.00268621, 'atl_change_percentage': 19707.86295, 'atl_date': '2014-05-22T00:00:00.000Z', 'roi': None, 'last_updated': '2024-10-08T00:54:11.049Z'}, {'id': 'staked-ether', 'symbol': 'steth', 'name': 'Lido Staked Ether', 'image': 'https://coin-images.coingecko.com/coins/images/13442/large/steth_logo.png?1696513206', 'current_price': 2433.54, 'market_cap': 23774301241, 'market_cap_rank': 8, 'fully_diluted_valuation': 23774301241, 'total_volume': 55393440, 'high_24h': 2515.61, 'low_24h': 2405.33, 'price_change_24h': -52.13318341543163, 'price_change_percentage_24h': -2.09734, 'market_cap_change_24h': -622209584.8816986, 'market_cap_change_percentage_24h': -2.5504, 'circulating_supply': 9767026.36285491, 'total_supply': 9767026.36285491, 'max_supply': None, 'ath': 4829.57, 'ath_change_percentage': -49.57783, 'ath_date': '2021-11-10T14:40:47.256Z', 'atl': 482.9, 'atl_change_percentage': 404.28528, 'atl_date': '2020-12-22T04:08:21.854Z', 'roi': None, 'last_updated': '2024-10-08T00:54:10.810Z'}, {'id': 'dogecoin', 'symbol': 'doge', 'name': 'Dogecoin', 'image': 'https://coin-images.coingecko.com/coins/images/5/large/dogecoin.png?1696501409', 'current_price': 0.109138, 'market_cap': 15967242014, 'market_cap_rank': 9, 'fully_diluted_valuation': 15967846781, 'total_volume': 994792895, 'high_24h': 0.11513, 'low_24h': 0.108236, 'price_change_24h': -0.004391041308727209, 'price_change_percentage_24h': -3.86778, 'market_cap_change_24h': -629528269.0053463, 'market_cap_change_percentage_24h': -3.79308, 'circulating_supply': 146268586383.705, 'total_supply': 146274126383.705, 'max_supply': None, 'ath': 0.731578, 'ath_change_percentage': -85.08287, 'ath_date': '2021-05-08T05:08:23.458Z', 'atl': 8.69e-05, 'atl_change_percentage': 125476.21875, 'atl_date': '2015-05-06T00:00:00.000Z', 'roi': None, 'last_updated': '2024-10-08T00:54:07.023Z'}, {'id': 'tron', 'symbol': 'trx', 'name': 'TRON', 'image': 'https://coin-images.coingecko.com/coins/images/1094/large/tron-logo.png?1696502193', 'current_price': 0.156189, 'market_cap': 13526695367, 'market_cap_rank': 10, 'fully_diluted_valuation': 13526702713, 'total_volume': 382377142, 'high_24h': 0.156897, 'low_24h': 0.153597, 'price_change_24h': 0.00148677, 'price_change_percentage_24h': 0.96105, 'market_cap_change_24h': 119102470, 'market_cap_change_percentage_24h': 0.88832, 'circulating_supply': 86577032111.108, 'total_supply': 86577079124.0223, 'max_supply': None, 'ath': 0.231673, 'ath_change_percentage': -32.53334, 'ath_date': '2018-01-05T00:00:00.000Z', 'atl': 0.00180434, 'atl_change_percentage': 8562.54313, 'atl_date': '2017-11-12T00:00:00.000Z', 'roi': {'times': 81.20479655054011, 'currency': 'usd', 'percentage': 8120.47965505401}, 'last_updated': '2024-10-08T00:54:05.672Z'}, {'id': 'the-open-network', 'symbol': 'ton', 'name': 'Toncoin', 'image': 'https://coin-images.coingecko.com/coins/images/17980/large/photo_2024-09-10_17.09.00.jpeg?1725963446', 'current_price': 5.23, 'market_cap': 13266378590, 'market_cap_rank': 11, 'fully_diluted_valuation': 26745582651, 'total_volume': 289898643, 'high_24h': 5.38, 'low_24h': 5.19, 'price_change_24h': -0.11581596106146907, 'price_change_percentage_24h': -2.1656, 'market_cap_change_24h': -292964162.0925293, 'market_cap_change_percentage_24h': -2.16061, 'circulating_supply': 2536031815.95331, 'total_supply': 5112747844.44798, 'max_supply': None, 'ath': 8.25, 'ath_change_percentage': -36.61515, 'ath_date': '2024-06-15T00:36:51.509Z', 'atl': 0.519364, 'atl_change_percentage': 907.29703, 'atl_date': '2021-09-21T00:33:11.092Z', 'roi': None, 'last_updated': '2024-10-08T00:54:13.375Z'}, {'id': 'cardano', 'symbol': 'ada', 'name': 'Cardano', 'image': 'https://coin-images.coingecko.com/coins/images/975/large/cardano.png?1696502090', 'current_price': 0.35311, 'market_cap': 12609866405, 'market_cap_rank': 12, 'fully_diluted_valuation': 15891641222, 'total_volume': 323896654, 'high_24h': 0.36763, 'low_24h': 0.350159, 'price_change_24h': -0.007715970856781273, 'price_change_percentage_24h': -2.13842, 'market_cap_change_24h': 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'2024-06-09T12:55:51.835Z', 'atl': 0.00084753, 'atl_change_percentage': 10308.88083, 'atl_date': '2024-02-29T08:40:24.951Z', 'roi': None, 'last_updated': '2024-10-08T00:54:05.104Z'}, {'id': 'coredaoorg', 'symbol': 'core', 'name': 'Core', 'image': 'https://coin-images.coingecko.com/coins/images/28938/large/file_589.jpg?1701868471', 'current_price': 0.937112, 'market_cap': 858110315, 'market_cap_rank': 89, 'fully_diluted_valuation': 1971753958, 'total_volume': 31657462, 'high_24h': 0.967114, 'low_24h': 0.932028, 'price_change_24h': -0.01422813882470342, 'price_change_percentage_24h': -1.49559, 'market_cap_change_24h': -10961374.97734642, 'market_cap_change_percentage_24h': -1.26127, 'circulating_supply': 913923187.211838, 'total_supply': 2100000000.0, 'max_supply': 2100000000.0, 'ath': 6.14, 'ath_change_percentage': -84.69421, 'ath_date': '2023-02-08T12:55:39.828Z', 'atl': 0.334237, 'atl_change_percentage': 181.33666, 'atl_date': '2023-11-03T01:20:55.441Z', 'roi': None, 'last_updated': '2024-10-08T00:54:07.590Z'}, {'id': 'fasttoken', 'symbol': 'ftn', 'name': 'Fasttoken', 'image': 'https://coin-images.coingecko.com/coins/images/28478/large/lightenicon_200x200.png?1696527472', 'current_price': 2.6, 'market_cap': 856233446, 'market_cap_rank': 90, 'fully_diluted_valuation': 2290629881, 'total_volume': 215412570, 'high_24h': 2.62, 'low_24h': 2.51, 'price_change_24h': 0.062821, 'price_change_percentage_24h': 2.47271, 'market_cap_change_24h': 21132723, 'market_cap_change_percentage_24h': 2.53056, 'circulating_supply': 328942461.97373456, 'total_supply': 880000000.0, 'max_supply': 1000000000.0, 'ath': 2.62, 'ath_change_percentage': -0.4686, 'ath_date': '2024-10-07T19:07:59.036Z', 'atl': 0.398142, 'atl_change_percentage': 553.93665, 'atl_date': '2023-01-21T10:15:39.503Z', 'roi': None, 'last_updated': '2024-10-08T00:54:08.935Z'}, {'id': 'gala', 'symbol': 'gala', 'name': 'GALA', 'image': 'https://coin-images.coingecko.com/coins/images/12493/large/GALA_token_image_-_200PNG.png?1709725869', 'current_price': 0.02149496, 'market_cap': 848757809, 'market_cap_rank': 91, 'fully_diluted_valuation': 848757654, 'total_volume': 161097632, 'high_24h': 0.02224224, 'low_24h': 0.02072146, 'price_change_24h': 0.00021638, 'price_change_percentage_24h': 1.01687, 'market_cap_change_24h': 9005263, 'market_cap_change_percentage_24h': 1.07237, 'circulating_supply': 39481499168.913, 'total_supply': 39481491922.3056, 'max_supply': 50000000000.0, 'ath': 0.824837, 'ath_change_percentage': -97.39249, 'ath_date': '2021-11-26T01:03:48.731Z', 'atl': 0.00013475, 'atl_change_percentage': 15861.25537, 'atl_date': '2020-12-28T08:46:48.367Z', 'roi': None, 'last_updated': '2024-10-08T00:54:09.522Z'}, {'id': 'ether-fi-staked-eth', 'symbol': 'eeth', 'name': 'ether.fi Staked ETH', 'image': 'https://coin-images.coingecko.com/coins/images/33049/large/ether.fi_eETH.png?1700473063', 'current_price': 2424.85, 'market_cap': 846755524, 'market_cap_rank': 92, 'fully_diluted_valuation': 4871591578, 'total_volume': 106209, 'high_24h': 2508.25, 'low_24h': 2415.47, 'price_change_24h': -41.29035543839427, 'price_change_percentage_24h': -1.67429, 'market_cap_change_24h': -18371264.200617433, 'market_cap_change_percentage_24h': -2.12353, 'circulating_supply': 349139.81169378624, 'total_supply': 2008686.70827969, 'max_supply': None, 'ath': 5307.23, 'ath_change_percentage': -54.27135, 'ath_date': '2024-08-06T16:16:30.007Z', 'atl': 2155.76, 'atl_change_percentage': 12.57876, 'atl_date': '2024-02-08T11:26:30.368Z', 'roi': None, 'last_updated': '2024-10-08T00:54:07.140Z'}, {'id': 'wormhole', 'symbol': 'w', 'name': 'Wormhole', 'image': 'https://coin-images.coingecko.com/coins/images/35087/large/womrhole_logo_full_color_rgb_2000px_72ppi_fb766ac85a.png?1708688954', 'current_price': 0.324805, 'market_cap': 836255956, 'market_cap_rank': 93, 'fully_diluted_valuation': 3243975804, 'total_volume': 219924753, 'high_24h': 0.35574, 'low_24h': 0.323118, 'price_change_24h': -0.02045301877589417, 'price_change_percentage_24h': -5.92398, 'market_cap_change_24h': -51338984.688058615, 'market_cap_change_percentage_24h': -5.78406, 'circulating_supply': 2577873594.0, 'total_supply': 10000000000.0, 'max_supply': 10000000000.0, 'ath': 1.66, 'ath_change_percentage': -80.38595, 'ath_date': '2024-04-03T11:46:35.308Z', 'atl': 0.16434, 'atl_change_percentage': 97.84984, 'atl_date': '2024-08-05T06:26:45.610Z', 'roi': None, 'last_updated': '2024-10-08T00:53:48.826Z'}, {'id': 'flow', 'symbol': 'flow', 'name': 'Flow', 'image': 'https://coin-images.coingecko.com/coins/images/13446/large/5f6294c0c7a8cda55cb1c936_Flow_Wordmark.png?1696513210', 'current_price': 0.540995, 'market_cap': 831299188, 'market_cap_rank': 94, 'fully_diluted_valuation': 831299188, 'total_volume': 58056753, 'high_24h': 0.561971, 'low_24h': 0.53686, 'price_change_24h': -0.01656192450478211, 'price_change_percentage_24h': -2.97044, 'market_cap_change_24h': -24892946.25119114, 'market_cap_change_percentage_24h': -2.9074, 'circulating_supply': 1536086728.20855, 'total_supply': 1536086728.20855, 'max_supply': None, 'ath': 42.4, 'ath_change_percentage': -98.72532, 'ath_date': '2021-04-05T13:49:10.098Z', 'atl': 0.391969, 'atl_change_percentage': 37.87491, 'atl_date': '2023-09-11T19:41:06.528Z', 'roi': None, 'last_updated': '2024-10-08T00:54:08.411Z'}, {'id': 'notcoin', 'symbol': 'not', 'name': 'Notcoin', 'image': 'https://coin-images.coingecko.com/coins/images/33453/large/rFmThDiD_400x400.jpg?1701876350', 'current_price': 0.00801109, 'market_cap': 822138290, 'market_cap_rank': 95, 'fully_diluted_valuation': 822138290, 'total_volume': 264338630, 'high_24h': 0.00839936, 'low_24h': 0.00772693, 'price_change_24h': 0.00011801, 'price_change_percentage_24h': 1.49515, 'market_cap_change_24h': 13636202, 'market_cap_change_percentage_24h': 1.6866, 'circulating_supply': 102474422538.643, 'total_supply': 102474422538.643, 'max_supply': None, 'ath': 0.02836145, 'ath_change_percentage': -71.61676, 'ath_date': '2024-06-02T18:00:38.587Z', 'atl': 0.00461057, 'atl_change_percentage': 74.59678, 'atl_date': '2024-05-24T07:12:14.147Z', 'roi': None, 'last_updated': '2024-10-08T00:54:11.088Z'}, {'id': 'beam-2', 'symbol': 'beam', 'name': 'Beam', 'image': 'https://coin-images.coingecko.com/coins/images/32417/large/chain-logo.png?1698114384', 'current_price': 0.01552574, 'market_cap': 801595531, 'market_cap_rank': 96, 'fully_diluted_valuation': 968773026, 'total_volume': 39916618, 'high_24h': 0.01674658, 'low_24h': 0.01538882, 'price_change_24h': -0.000940684073936141, 'price_change_percentage_24h': -5.71274, 'market_cap_change_24h': -47924263.662335515, 'market_cap_change_percentage_24h': -5.64134, 'circulating_supply': 51660007768.0, 'total_supply': 62434008330.0, 'max_supply': 62434008330.0, 'ath': 0.04416304, 'ath_change_percentage': -64.79328, 'ath_date': '2024-03-10T10:40:22.381Z', 'atl': 0.0043383, 'atl_change_percentage': 258.39735, 'atl_date': '2023-10-29T08:20:14.064Z', 'roi': None, 'last_updated': '2024-10-08T00:54:13.203Z'}, {'id': 'renzo-restaked-eth', 'symbol': 'ezeth', 'name': 'Renzo Restaked ETH', 'image': 'https://coin-images.coingecko.com/coins/images/34753/large/Ezeth_logo_circle.png?1713496404', 'current_price': 2482.56, 'market_cap': 794760666, 'market_cap_rank': 97, 'fully_diluted_valuation': 794760666, 'total_volume': 11266087, 'high_24h': 2562.78, 'low_24h': 2455.06, 'price_change_24h': -51.8338887688119, 'price_change_percentage_24h': -2.04522, 'market_cap_change_24h': -22560571.011731625, 'market_cap_change_percentage_24h': -2.76031, 'circulating_supply': 320078.067454777, 'total_supply': 320078.067454777, 'max_supply': None, 'ath': 4106.74, 'ath_change_percentage': -39.47159, 'ath_date': '2024-03-12T00:44:29.429Z', 'atl': 2198.04, 'atl_change_percentage': 13.08921, 'atl_date': '2024-01-26T08:09:59.848Z', 'roi': None, 'last_updated': '2024-10-08T00:54:10.315Z'}, {'id': 'ethena', 'symbol': 'ena', 'name': 'Ethena', 'image': 'https://coin-images.coingecko.com/coins/images/36530/large/ethena.png?1711701436', 'current_price': 0.287191, 'market_cap': 789001209, 'market_cap_rank': 98, 'fully_diluted_valuation': 4308539023, 'total_volume': 209536876, 'high_24h': 0.311956, 'low_24h': 0.283762, 'price_change_24h': -0.013023047337344384, 'price_change_percentage_24h': -4.33793, 'market_cap_change_24h': -35648060.95506203, 'market_cap_change_percentage_24h': -4.32281, 'circulating_supply': 2746875000.0, 'total_supply': 15000000000.0, 'max_supply': None, 'ath': 1.52, 'ath_change_percentage': -81.04188, 'ath_date': '2024-04-11T13:15:15.057Z', 'atl': 0.195078, 'atl_change_percentage': 47.4937, 'atl_date': '2024-09-06T21:04:48.253Z', 'roi': None, 'last_updated': '2024-10-08T00:54:04.226Z'}, {'id': 'klay-token', 'symbol': 'klay', 'name': 'Klaytn', 'image': 'https://coin-images.coingecko.com/coins/images/9672/large/klaytn.png?1696509742', 'current_price': 0.132756, 'market_cap': 770707685, 'market_cap_rank': 99, 'fully_diluted_valuation': 774944601, 'total_volume': 15981912, 'high_24h': 0.13638, 'low_24h': 0.131797, 'price_change_24h': -0.002995926554336559, 'price_change_percentage_24h': -2.20692, 'market_cap_change_24h': -19069806.84438181, 'market_cap_change_percentage_24h': -2.41458, 'circulating_supply': 5806693656.94806, 'total_supply': 5838615577.49937, 'max_supply': None, 'ath': 4.34, 'ath_change_percentage': -96.93899, 'ath_date': '2021-03-30T03:44:28.828Z', 'atl': 0.06044, 'atl_change_percentage': 119.7475, 'atl_date': '2020-04-29T08:19:34.574Z', 'roi': None, 'last_updated': '2024-10-08T00:54:11.218Z'}, {'id': 'aerodrome-finance', 'symbol': 'aero', 'name': 'Aerodrome Finance', 'image': 'https://coin-images.coingecko.com/coins/images/31745/large/token.png?1696530564', 'current_price': 1.2, 'market_cap': 769656411, 'market_cap_rank': 100, 'fully_diluted_valuation': 1557648682, 'total_volume': 55612141, 'high_24h': 1.27, 'low_24h': 1.19, 'price_change_24h': -0.0154197910139926, 'price_change_percentage_24h': -1.26784, 'market_cap_change_24h': -9478210.726666927, 'market_cap_change_percentage_24h': -1.2165, 'circulating_supply': 641571846.36209, 'total_supply': 1298428138.12827, 'max_supply': None, 'ath': 2.31, 'ath_change_percentage': -48.0214, 'ath_date': '2024-04-12T03:44:52.080Z', 'atl': 1.861e-05, 'atl_change_percentage': 6440257.63163, 'atl_date': '2023-10-17T01:23:50.860Z', 'roi': None, 'last_updated': '2024-10-08T00:54:07.760Z'}]
# Loop through the data and print the name and current price
for coin in data:
    name = coin['name']
    price = coin['current_price']
    print(f"Coin: {name}, Price: ${price}")
Coin: Bitcoin, Price: $62490
Coin: Ethereum, Price: $2434.1
Coin: Tether, Price: $0.999711
Coin: BNB, Price: $568.59
Coin: Solana, Price: $144.62
Coin: USDC, Price: $0.99982
Coin: XRP, Price: $0.531761
Coin: Lido Staked Ether, Price: $2433.54
Coin: Dogecoin, Price: $0.109138
Coin: TRON, Price: $0.156189
Coin: Toncoin, Price: $5.23
Coin: Cardano, Price: $0.35311
Coin: Avalanche, Price: $26.86
Coin: Shiba Inu, Price: $1.759e-05
Coin: Wrapped stETH, Price: $2870.49
Coin: Wrapped Bitcoin, Price: $62339
Coin: WETH, Price: $2434.35
Coin: Chainlink, Price: $11.22
Coin: Bitcoin Cash, Price: $325.66
Coin: Polkadot, Price: $4.15
Coin: Dai, Price: $0.999811
Coin: Sui, Price: $2.06
Coin: NEAR Protocol, Price: $5.11
Coin: LEO Token, Price: $6.01
Coin: Uniswap, Price: $7.24
Coin: Litecoin, Price: $65.04
Coin: Bittensor, Price: $617.07
Coin: Aptos, Price: $8.91
Coin: Pepe, Price: $9.89e-06
Coin: Wrapped eETH, Price: $2554.88
Coin: Artificial Superintelligence Alliance, Price: $1.49
Coin: Internet Computer, Price: $8.13
Coin: Kaspa, Price: $0.137693
Coin: POL (ex-MATIC), Price: $0.376926
Coin: Ethereum Classic, Price: $18.7
Coin: Stellar, Price: $0.091505
Coin: Monero, Price: $144.06
Coin: Stacks, Price: $1.77
Coin: First Digital USD, Price: $0.999501
Coin: dogwifhat, Price: $2.56
Coin: OKB, Price: $41.75
Coin: Ethena USDe, Price: $0.998868
Coin: Immutable, Price: $1.49
Coin: Filecoin, Price: $3.74
Coin: Aave, Price: $146.68
Coin: Cronos, Price: $0.078844
Coin: Optimism, Price: $1.67
Coin: Render, Price: $5.32
Coin: Injective, Price: $20.63
Coin: Arbitrum, Price: $0.55172
Coin: Hedera, Price: $0.052714
Coin: Mantle, Price: $0.594723
Coin: Fantom, Price: $0.680296
Coin: VeChain, Price: $0.02305745
Coin: Cosmos Hub, Price: $4.44
Coin: THORChain, Price: $5.09
Coin: WhiteBIT Coin, Price: $11.61
Coin: The Graph, Price: $0.16643
Coin: Sei, Price: $0.436364
Coin: Bitget Token, Price: $1.075
Coin: Bonk, Price: $2.134e-05
Coin: Binance-Peg WETH, Price: $2434.55
Coin: FLOKI, Price: $0.00013831
Coin: Rocket Pool ETH, Price: $2719.44
Coin: Theta Network, Price: $1.31
Coin: Popcat, Price: $1.28
Coin: Arweave, Price: $19.03
Coin: Maker, Price: $1406.89
Coin: Mantle Staked Ether, Price: $2540.6
Coin: MANTRA, Price: $1.4
Coin: Pyth Network, Price: $0.327718
Coin: Helium, Price: $6.89
Coin: Solv Protocol SolvBTC, Price: $62563
Coin: Celestia, Price: $5.39
Coin: Gate, Price: $8.86
Coin: Jupiter, Price: $0.772541
Coin: Algorand, Price: $0.125351
Coin: Polygon, Price: $0.377139
Coin: Ondo, Price: $0.711239
Coin: Worldcoin, Price: $1.95
Coin: Quant, Price: $67.87
Coin: Lido DAO, Price: $1.079
Coin: KuCoin, Price: $7.95
Coin: JasmyCoin, Price: $0.01934656
Coin: Bitcoin SV, Price: $45.98
Coin: Conflux, Price: $0.198997
Coin: BitTorrent, Price: $9.23941e-07
Coin: Brett, Price: $0.088115
Coin: Core, Price: $0.937112
Coin: Fasttoken, Price: $2.6
Coin: GALA, Price: $0.02149496
Coin: ether.fi Staked ETH, Price: $2424.85
Coin: Wormhole, Price: $0.324805
Coin: Flow, Price: $0.540995
Coin: Notcoin, Price: $0.00801109
Coin: Beam, Price: $0.01552574
Coin: Renzo Restaked ETH, Price: $2482.56
Coin: Ethena, Price: $0.287191
Coin: Klaytn, Price: $0.132756
Coin: Aerodrome Finance, Price: $1.2

5.6 Independent Study

5.6.1 Practice exercise 1: Reading .csv data

Read the file Top 10 Albums By Year.csv. This file contains the top 10 albums for each year from 1990 to 2021. Each row corresponds to a unique album.

5.6.1.1

Print the first 5 rows of the data.

5.6.1.2

How many rows and columns are there in the data?

5.6.1.3

Print the summary statistics of the data, and answer the following questions:

  1. What proportion of albums have 15 or lesser tracks? Mention a range for the proportion.
  2. What is the mean length of a track (in minutes)?

5.6.1.4

Find the album having the highest worldwide sales per year, and its artist.

5.6.1.5

Subset the data to include only Hip-Hop albums. How many Hip_Hop albums are there?

5.6.1.6

Which album amongst hip-hop has the higest mean sales per year per track, and who is its artist?

5.6.2 Practice exercise 2: Reading .txt data

Read the file bestseller_books.txt. It contains top 50 best-selling books on amazon from 2009 to 2019. Identify the delimiter without opening the file with Notepad or a text-editing software. How many rows and columns are there in the dataset?

Solution:

#The delimiter seems to be ';' based on the output of the above code
bestseller_books = pd.read_csv('../Data/bestseller_books.txt',sep=';')
bestseller_books.head()
Unnamed: 0.1 Unnamed: 0 Name Author User Rating Reviews Price Year Genre
0 0 0 10-Day Green Smoothie Cleanse JJ Smith 4.7 17350 8 2016 Non Fiction
1 1 1 11/22/63: A Novel Stephen King 4.6 2052 22 2011 Fiction
2 2 2 12 Rules for Life: An Antidote to Chaos Jordan B. Peterson 4.7 18979 15 2018 Non Fiction
3 3 3 1984 (Signet Classics) George Orwell 4.7 21424 6 2017 Fiction
4 4 4 5,000 Awesome Facts (About Everything!) (Natio... National Geographic Kids 4.8 7665 12 2019 Non Fiction 
#The file read with ';' as the delimited is correct
print("The file has",bestseller_books.shape[0],"rows and",bestseller_books.shape[1],"columns")
The file has 550 rows and 9 columns

Alternatively, you can use the argument sep = None, and engine = 'python'. The default engine is C. However, the ‘python’ engine has a ‘sniffer’ tool which may identify the delimiter automatically.

bestseller_books = pd.read_csv('../data/bestseller_books.txt',sep=None, engine = 'python')
bestseller_books.head()
Unnamed: 0.1 Unnamed: 0 Name Author User Rating Reviews Price Year Genre
0 0 0 10-Day Green Smoothie Cleanse JJ Smith 4.7 17350 8 2016 Non Fiction
1 1 1 11/22/63: A Novel Stephen King 4.6 2052 22 2011 Fiction
2 2 2 12 Rules for Life: An Antidote to Chaos Jordan B. Peterson 4.7 18979 15 2018 Non Fiction
3 3 3 1984 (Signet Classics) George Orwell 4.7 21424 6 2017 Fiction
4 4 4 5,000 Awesome Facts (About Everything!) (Natio... National Geographic Kids 4.8 7665 12 2019 Non Fiction

5.6.3 Practice exercise 3: Reading HTML data

Read the table(s) consisting of attendance of spectators in FIFA worlds cup from this page. Read only those table(s) that have the word ‘attendance’ in them. How many rows and columns are there in the table(s)?

dfs = pd.read_html('https://en.wikipedia.org/wiki/FIFA_World_Cup',
                       match='reaching')
print(len(dfs))
data = dfs[0]
print("Number of rows =",data.shape[0], "and number of columns=",data.shape[1])
data.head()
1
Number of rows = 25 and number of columns= 6
Team Titles Runners-up Third place Fourth place Top 4 total
0 Brazil 5 (1958, 1962, 1970, 1994, 2002) 2 (1950 *, 1998) 2 (1938, 1978) 2 (1974, 2014 *) 11
1 Germany1 4 (1954, 1974 *, 1990, 2014) 4 (1966, 1982, 1986, 2002) 4 (1934, 1970, 2006 *, 2010) 1 (1958) 13
2 Italy 4 (1934 *, 1938, 1982, 2006) 2 (1970, 1994) 1 (1990 *) 1 (1978) 8
3 Argentina 3 (1978 *, 1986, 2022) 3 (1930, 1990, 2014) NaN NaN 6
4 France 2 (1998 *, 2018) 2 (2006, 2022) 2 (1958, 1986) 1 (1982) 7

5.6.4 Practice exercise 4: Reading JSON data

Read the movies dataset from here. How many rows and columns are there in the data?

movies_data = pd.read_json('https://raw.githubusercontent.com/vega/vega-datasets/master/data/movies.json')
print("Number of rows =",movies_data.shape[0], "and number of columns=",movies_data.shape[1])
Number of rows = 3201 and number of columns= 16