Data Science Toolbox : Python
This is part of my learning journey of data science with python through Datacamp.
Using zip
# Create a list of tuples: mutant_data
mutant_data = list(zip(mutants,aliases,powers))
# Print the list of tuples
print(mutant_data)
# Create a zip object using the three lists: mutant_zip
mutant_zip = zip(mutants,aliases,powers)
# Print the zip object
print(mutant_zip)
# Unpack the zip object and print the tuple values
for value1, value2, value3 in mutant_zip:
print(value1, value2, value3)
Output
[('charles xavier', 'prof x', 'telepathy'), ('bobby drake', 'iceman', 'thermokinesis'), ('kurt wagner', 'nightcrawler', 'teleportation'), ('max eisenhardt', 'magneto', 'magnetokinesis'), ('kitty pryde', 'shadowcat', 'intangibility')] <zip object at 0x7fd0e861c2c8> charles xavier prof x telepathy bobby drake iceman thermokinesis kurt wagner nightcrawler teleportation max eisenhardt magneto magnetokinesis kitty pryde shadowcat intangibility
Using enumerate
# Create a list of strings: mutants
mutants = ['charles xavier',
'bobby drake',
'kurt wagner',
'max eisenhardt',
'kitty pryde']
# Create a list of tuples: mutant_list
mutant_list = list(enumerate(mutants))
# Print the list of tuples
print(mutant_list)
# Unpack and print the tuple pairs
for index1,value1 in enumerate(mutants):
print(index1, value1)
# Change the start index
for index2,value2 in enumerate(mutants,start=1):
print(index2, value2)
Output
[(0, 'charles xavier'), (1, 'bobby drake'), (2, 'kurt wagner'), (3, 'max eisenhardt'), (4, 'kitty pryde')] 0 charles xavier 1 bobby drake 2 kurt wagner 3 max eisenhardt 4 kitty pryde 1 charles xavier 2 bobby drake 3 kurt wagner 4 max eisenhardt 5 kitty pryde
Using * and zip to unzip
# Create a zip object from mutants and powers: z1
z1 = zip(mutants,powers)
# Print the tuples in z1 by unpacking with *
print(*z1)
# Re-create a zip object from mutants and powers: z1
z1 = zip(mutants,powers)
# 'Unzip' the tuples in z1 by unpacking with * and zip(): result1, result2
result1, result2 = zip(*z1)
# Check if unpacked tuples are equivalent to original tuples
print(result1 == mutants)
print(result2 == powers)
Output
('charles xavier', 'telepathy') ('bobby drake', 'thermokinesis') ('kurt wagner', 'teleportation') ('max eisenhardt', 'magnetokinesis') ('kitty pryde', 'intangibility') True True
Processing large amounts of Twitter data (a csv file)
# Initialize an empty dictionary: counts_dict
counts_dict={}
# Iterate over the file chunk by chunk
for chunk in pd.read_csv('tweets.csv',chunksize=10):
# Iterate over the column in DataFrame
for entry in chunk['lang']:
if entry in counts_dict.keys():
counts_dict[entry] += 1
else:
counts_dict[entry] = 1
# Print the populated dictionary
print(counts_dict)
Output
{'en': 97, 'et': 1, 'und': 2}# Define count_entries()
def count_entries(csv_file,c_size,colname):
"""Return a dictionary with counts of
occurrences as value for each key."""
# Initialize an empty dictionary: counts_dict
counts_dict = {}
# Iterate over the file chunk by chunk
for chunk in pd.read_csv(csv_file,chunksize=c_size):
# Iterate over the column in DataFrame
for entry in chunk[colname]:
if entry in counts_dict.keys():
counts_dict[entry] += 1
else:
counts_dict[entry] = 1
# Return counts_dict
return counts_dict
# Call count_entries(): result_counts
result_counts = count_entries('tweets.csv',10,'lang')
# Print result_counts
print(result_counts)
Output
{'en': 97, 'et': 1, 'und': 2}Write list comprehension
# Create list comprehension: squares
squares = [i**2 for i in range(10)]
print(squares)
Output
[0, 1, 4, 9, 16, 25, 36, 49, 64, 81]
Nested list comprehensions
# Create a 5 x 5 matrix using a list of lists: matrix
matrix = [[col for col in range(5)] for row in range(5)]
# Print the matrix
for row in matrix:
print(row)
Output
[0, 1, 2, 3, 4] [0, 1, 2, 3, 4] [0, 1, 2, 3, 4] [0, 1, 2, 3, 4] [0, 1, 2, 3, 4]
Using conditions in list comprehension
# Create a list of strings: fellowship
fellowship = ['frodo', 'samwise', 'merry', 'aragorn', 'legolas', 'boromir', 'gimli']
# Create list comprehension: new_fellowship
new_fellowship = [member for member in fellowship if len(member)>=7]
# Print the new list
print(new_fellowship)
Output
['samwise', 'aragorn', 'legolas', 'boromir']
# Create a list of strings: fellowship
fellowship = ['frodo', 'samwise', 'merry', 'aragorn', 'legolas', 'boromir', 'gimli']
# Create list comprehension: new_fellowship
new_fellowship = [member if len(member)>=7 else '' for member in fellowship]
# Print the new list
print(new_fellowship)
['', 'samwise', '', 'aragorn', 'legolas', 'boromir', '']
Dictionary comprehensions
# Create a list of strings: fellowship
fellowship = ['frodo', 'samwise', 'merry', 'aragorn', 'legolas', 'boromir', 'gimli']
# Create dict comprehension: new_fellowship
new_fellowship = {member:len(member) for member in fellowship}
# Print the new dictionary
print(new_fellowship)
Output
{'frodo': 5, 'samwise': 7, 'merry': 5, 'aragorn': 7, 'legolas': 7, 'boromir': 7, 'gimli': 5}Generator expression
# Create generator object: result
result = (num for num in range(31))
# Print the first 5 values
print(next(result))
print(next(result))
print(next(result))
print(next(result))
print(next(result))
# Print the rest of the values
for value in result:
print(value)
# Create a list of strings: lannister
lannister = ['cersei', 'jaime', 'tywin', 'tyrion', 'joffrey']
# Create a generator object: lengths
lengths = (len(person) for person in lannister)
# Iterate over and print the values in lengths
for value in lengths:
print(value)
6 5 5 6 7
Generator function
# Create a list of strings
lannister = ['cersei', 'jaime', 'tywin', 'tyrion', 'joffrey']
# Define generator function get_lengths
def get_lengths(input_list):
"""Generator function that yields the
length of the strings in input_list."""
# Yield the length of a string
for person in input_list:
input_list=yield(len(person))
# Print the values generated by get_lengths()
for value in get_lengths(lannister):
print(value)
6 5 5 6 7
List comprehension for time-stamped data
# Extract the created_at column from df: tweet_time
tweet_time = df['created_at']
# Extract the clock time: tweet_clock_time
tweet_clock_time = [entry[11:19] for entry in tweet_time]
# Print the extracted times
print(tweet_clock_time)
Output
['23:40:17', '23:40:17', '23:40:17', '23:40:17', '23:40:17', '23:40:17', '23:40:18', '23:40:17', '23:40:18', '23:40:18', '23:40:18', '23:40:17', '23:40:18', '23:40:18', '23:40:17', '23:40:18', '23:40:18', '23:40:17', '23:40:18', '23:40:17', '23:40:18', '23:40:18', '23:40:18', '23:40:18', '23:40:17', '23:40:18', '23:40:18', '23:40:17', '23:40:18', '23:40:18', '23:40:18', '23:40:18', '23:40:18', '23:40:18', '23:40:18', '23:40:18', '23:40:18', '23:40:18', '23:40:18', '23:40:18', '23:40:18', '23:40:18', '23:40:18', '23:40:18', '23:40:18', '23:40:18', '23:40:18', '23:40:18', '23:40:18', '23:40:18', '23:40:18', '23:40:18', '23:40:18', '23:40:18', '23:40:18', '23:40:18', '23:40:18', '23:40:18', '23:40:18', '23:40:18', '23:40:19', '23:40:18', '23:40:18', '23:40:18', '23:40:19', '23:40:19', '23:40:19', '23:40:18', '23:40:19', '23:40:19', '23:40:19', '23:40:18', '23:40:19', '23:40:19', '23:40:19', '23:40:18', '23:40:19', '23:40:19', '23:40:19', '23:40:19', '23:40:19', '23:40:19', '23:40:19', '23:40:19', '23:40:19', '23:40:19', '23:40:19', '23:40:19', '23:40:19', '23:40:19', '23:40:19', '23:40:19', '23:40:19', '23:40:19', '23:40:19', '23:40:19', '23:40:19', '23:40:19', '23:40:19', '23:40:19']
Condiational List comprehension for time-stamped data
# Extract the created_at column from df: tweet_time
tweet_time = df['created_at']
# Extract the clock time: tweet_clock_time
tweet_clock_time = [entry[11:19] for entry in tweet_time if entry[17:19] == '19']
# Print the extracted times
print(tweet_clock_time)
Output
['23:40:19', '23:40:19', '23:40:19', '23:40:19', '23:40:19', '23:40:19', '23:40:19', '23:40:19', '23:40:19', '23:40:19', '23:40:19', '23:40:19', '23:40:19', '23:40:19', '23:40:19', '23:40:19', '23:40:19', '23:40:19', '23:40:19', '23:40:19', '23:40:19', '23:40:19', '23:40:19', '23:40:19', '23:40:19', '23:40:19', '23:40:19', '23:40:19', '23:40:19', '23:40:19', '23:40:19', '23:40:19', '23:40:19', '23:40:19']
Dictionaries for data science
print(feature_names)
print(row_vals)
# Zip lists: zipped_lists
zipped_lists = zip(feature_names,row_vals)
# Create a dictionary: rs_dict
rs_dict = dict(zipped_lists)
# Print the dictionary
print(rs_dict)
Output
{'CountryName': 'Arab World', 'CountryCode': 'ARB', 'IndicatorName': 'Adolescent fertility rate (births per 1,000 women ages 15-19)', 'IndicatorCode': 'SP.ADO.TFRT', 'Year': '1960', 'Value': '133.56090740552298'}# Define lists2dict()
def lists2dict(list1, list2):
"""Return a dictionary where list1 provides
the keys and list2 provides the values."""
# Zip lists: zipped_lists
zipped_lists = zip(list1, list2)
# Create a dictionary: rs_dict
rs_dict = dict(zipped_lists)
# Return the dictionary
return rs_dict
# Call lists2dict: rs_fxn
rs_fxn = lists2dict(feature_names,row_vals)
# Print rs_fxn
print(rs_fxn)
Output
{'CountryName': 'Arab World', 'CountryCode': 'ARB', 'IndicatorName': 'Adolescent fertility rate (births per 1,000 women ages 15-19)', 'IndicatorCode': 'SP.ADO.TFRT', 'Year': '1960', 'Value': '133.56090740552298'}
Using a list comprehension
row_lists
# Print the first two lists in row_lists
print(row_lists[0])
print(row_lists[1])
# Turn list of lists into list of dicts: list_of_dicts
list_of_dicts = [lists2dict(feature_names,sublist) for sublist in row_lists]
# Print the first two dictionaries in list_of_dicts
print(list_of_dicts[0])
print(list_of_dicts[1])
Output
['Arab World', 'ARB', 'Adolescent fertility rate (births per 1,000 women ages 15-19)', 'SP.ADO.TFRT', '1960', '133.56090740552298']
['Arab World', 'ARB', 'Age dependency ratio (% of working-age population)', 'SP.POP.DPND', '1960', '87.7976011532547']
{'CountryName': 'Arab World', 'CountryCode': 'ARB', 'IndicatorName': 'Adolescent fertility rate (births per 1,000 women ages 15-19)', 'IndicatorCode': 'SP.ADO.TFRT', 'Year': '1960', 'Value': '133.56090740552298'}
{'CountryName': 'Arab World', 'CountryCode': 'ARB', 'IndicatorName': 'Age dependency ratio (% of working-age population)', 'IndicatorCode': 'SP.POP.DPND', 'Year': '1960', 'Value': '87.7976011532547'}Turning this all into a DataFrame
# Import the pandas package
import pandas as pd
# Turn list of lists into list of dicts: list_of_dicts
list_of_dicts = [lists2dict(feature_names, sublist) for sublist in row_lists]
# Turn list of dicts into a DataFrame: df
df = pd.DataFrame(list_of_dicts)
# Print the head of the DataFrame
print(df.head())
Output
CountryName CountryCode IndicatorName \
0 Arab World ARB Adolescent fertility rate (births per 1,000 wo...
1 Arab World ARB Age dependency ratio (% of working-age populat...
2 Arab World ARB Age dependency ratio, old (% of working-age po...
3 Arab World ARB Age dependency ratio, young (% of working-age ...
4 Arab World ARB Arms exports (SIPRI trend indicator values)
IndicatorCode Year Value
0 SP.ADO.TFRT 1960 133.56090740552298
1 SP.POP.DPND 1960 87.7976011532547
2 SP.POP.DPND.OL 1960 6.634579191565161
3 SP.POP.DPND.YG 1960 81.02332950839141
4 MS.MIL.XPRT.KD 1960 3000000.0Processing data in chunks (1)
# Open a connection to the file
with open('world_dev_ind.csv') as file:
# Skip the column names
file.readline()
# Initialize an empty dictionary: counts_dict
counts_dict = {}
# Process only the first 1000 rows
for j in range(1000):
# Split the current line into a list: line
line = file.readline().split(',')
# Get the value for the first column: first_col
first_col = line[0]
# If the column value is in the dict, increment its value
if first_col in counts_dict.keys():
counts_dict[first_col] += 1
# Else, add to the dict and set value to 1
else:
counts_dict[first_col] = 1
# Print the resulting dictionary
print(counts_dict)
Output
{'Arab World': 5, 'Caribbean small states': 5, 'Central Europe and the Baltics': 5, 'East Asia & Pacific (all income levels)': 5, 'East Asia & Pacific (developing only)': 5, 'Euro area': 5, 'Europe & Central Asia (all income levels)': 5, 'Europe & Central Asia (developing only)': 5, 'European Union': 5, 'Fragile and conflict affected situations': 5, 'Heavily indebted poor countries (HIPC)': 5, 'High income': 5, 'High income: nonOECD': 5, 'High income: OECD': 5, 'Latin America & Caribbean (all income levels)': 5, 'Latin America & Caribbean (developing only)': 5, 'Least developed countries: UN classification': 5, 'Low & middle income': 5, 'Low income': 5, 'Lower middle income': 5, 'Middle East & North Africa (all income levels)': 5, 'Middle East & North Africa (developing only)': 5, 'Middle income': 5, 'North America': 5, 'OECD members': 5, 'Other small states': 5, 'Pacific island small states': 5, 'Small states': 5, 'South Asia': 5, 'Sub-Saharan Africa (all income levels)': 5, 'Sub-Saharan Africa (developing only)': 5, 'Upper middle income': 5, 'World': 4, 'Afghanistan': 4, 'Albania': 4, 'Algeria': 4, 'American Samoa': 4, 'Andorra': 4, 'Angola': 4, 'Antigua and Barbuda': 4, 'Argentina': 4, 'Armenia': 4, 'Aruba': 4, 'Australia': 4, 'Austria': 4, 'Azerbaijan': 4, '"Bahamas': 4, 'Bahrain': 4, 'Bangladesh': 4, 'Barbados': 4, 'Belarus': 4, 'Belgium': 4, 'Belize': 4, 'Benin': 4, 'Bermuda': 4, 'Bhutan': 4, 'Bolivia': 4, 'Bosnia and Herzegovina': 4, 'Botswana': 4, 'Brazil': 4, 'Brunei Darussalam': 4, 'Bulgaria': 4, 'Burkina Faso': 4, 'Burundi': 4, 'Cabo Verde': 4, 'Cambodia': 4, 'Cameroon': 4, 'Canada': 4, 'Cayman Islands': 4, 'Central African Republic': 4, 'Chad': 4, 'Channel Islands': 4, 'Chile': 4, 'China': 4, 'Colombia': 4, 'Comoros': 4, '"Congo': 8, 'Costa Rica': 4, "Cote d'Ivoire": 4, 'Croatia': 4, 'Cuba': 4, 'Curacao': 4, 'Cyprus': 4, 'Czech Republic': 4, 'Denmark': 4, 'Djibouti': 4, 'Dominica': 4, 'Dominican Republic': 4, 'Ecuador': 4, '"Egypt': 4, 'El Salvador': 4, 'Equatorial Guinea': 4, 'Eritrea': 4, 'Estonia': 4, 'Ethiopia': 4, 'Faeroe Islands': 4, 'Fiji': 4, 'Finland': 4, 'France': 4, 'French Polynesia': 4, 'Gabon': 4, '"Gambia': 4, 'Georgia': 4, 'Germany': 4, 'Ghana': 4, 'Greece': 4, 'Greenland': 4, 'Grenada': 4, 'Guam': 4, 'Guatemala': 4, 'Guinea': 4, 'Guinea-Bissau': 4, 'Guyana': 4, 'Haiti': 4, 'Honduras': 4, '"Hong Kong SAR': 4, 'Hungary': 4, 'Iceland': 4, 'India': 4, 'Indonesia': 4, '"Iran': 4, 'Iraq': 4, 'Ireland': 4, 'Isle of Man': 4, 'Israel': 4, 'Italy': 4, 'Jamaica': 4, 'Japan': 4, 'Jordan': 4, 'Kazakhstan': 4, 'Kenya': 4, 'Kiribati': 4, '"Korea': 8, 'Kuwait': 4, 'Kyrgyz Republic': 4, 'Lao PDR': 4, 'Latvia': 4, 'Lebanon': 4, 'Lesotho': 4, 'Liberia': 4, 'Libya': 4, 'Liechtenstein': 4, 'Lithuania': 4, 'Luxembourg': 4, '"Macao SAR': 4, '"Macedonia': 4, 'Madagascar': 4, 'Malawi': 4, 'Malaysia': 4, 'Maldives': 4, 'Mali': 4, 'Malta': 4, 'Marshall Islands': 4, 'Mauritania': 4, 'Mauritius': 4, 'Mexico': 4, '"Micronesia': 4, 'Moldova': 4, 'Monaco': 4, 'Mongolia': 4, 'Montenegro': 4, 'Morocco': 4, 'Mozambique': 4, 'Myanmar': 4, 'Namibia': 4, 'Nepal': 4, 'Netherlands': 4, 'New Caledonia': 4, 'New Zealand': 4, 'Nicaragua': 4, 'Niger': 4, 'Nigeria': 4, 'Northern Mariana Islands': 4, 'Norway': 4, 'Oman': 4, 'Pakistan': 4, 'Palau': 4, 'Panama': 4, 'Papua New Guinea': 4, 'Paraguay': 4, 'Peru': 4, 'Philippines': 4, 'Poland': 4, 'Portugal': 4, 'Puerto Rico': 4, 'Qatar': 4, 'Romania': 4, 'Russian Federation': 4, 'Rwanda': 4, 'Samoa': 4, 'San Marino': 4, 'Sao Tome and Principe': 4, 'Saudi Arabia': 4, 'Senegal': 4, 'Seychelles': 4, 'Sierra Leone': 4, 'Singapore': 4, 'Slovak Republic': 4, 'Slovenia': 4, 'Solomon Islands': 4, 'Somalia': 4, 'South Africa': 4, 'South Sudan': 4, 'Spain': 4, 'Sri Lanka': 4, 'St. Kitts and Nevis': 4, 'St. Lucia': 4, 'St. Vincent and the Grenadines': 4, 'Sudan': 4, 'Suriname': 4, 'Swaziland': 4, 'Sweden': 4, 'Switzerland': 4, 'Syrian Arab Republic': 4, 'Tajikistan': 4, 'Tanzania': 4, 'Thailand': 4, 'Timor-Leste': 4, 'Togo': 4, 'Tonga': 4, 'Trinidad and Tobago': 4, 'Tunisia': 4, 'Turkey': 4, 'Turkmenistan': 4, 'Turks and Caicos Islands': 4, 'Tuvalu': 4, 'Uganda': 4, 'Ukraine': 4, 'United Arab Emirates': 4, 'United Kingdom': 4, 'United States': 4, 'Uruguay': 4, 'Uzbekistan': 4, 'Vanuatu': 4, '"Venezuela': 4, 'Vietnam': 4, 'Virgin Islands (U.S.)': 4, '"Yemen': 4, 'Zambia': 4, 'Zimbabwe': 4}Writing generator to load data in chunks
# Define read_large_file()
def read_large_file(file_object):
"""A generator function to read a large file lazily."""
# Loop indefinitely until the end of the file
while True:
# Read a line from the file: data
data = file_object.readline()
# Break if this is the end of the file
if not data:
break
# Yield the line of data
yield data
# Open a connection to the file
with open('world_dev_ind.csv') as file:
# Create a generator object for the file: gen_file
gen_file = read_large_file(file)
# Print the first three lines of the file
print(next(gen_file))
print(next(gen_file))
print(next(gen_file))
CountryName,CountryCode,Year,Total Population,Urban population (% of total) Arab World,ARB,1960,92495902.0,31.285384211605397 Caribbean small states,CSS,1960,4190810.0,31.5974898513652
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