Introduction to Data Science in Python
6 min readJan 6, 2022
Halo, kali ini, saya mau posting progress hasil belajar saya di DataCamp selama sepekan ini.
Dari 19 courses, saya baru berhasil menyelesaikan 3 courses tentang data analyst track in Python. Track pertama didahului mengenai pengenalan.
Ada 4 chapter pada pengenalan ini, yaitu:
- Getting started in Python
- Loading data in Pandas
- Plotting data with Matplotlib
- Different Types of Plot
Langsung saja, tampilan codes yang telah dikerjakan.
Getting Started in Python
# Use an import statement to import seaborn with alias sns
import seaborn as sns# Import statsmodels under the alias sm
import statsmodels as sm# Use an import statement to import seaborn with alias sns
import seaborn as sns# Fill in Bayes' age (4.0)
bayes_age = 4.0
# Display the variable bayes_age
print(bayes_age)# Bayes' favorite toy
favorite_toy = "Mr. Squeaky"
# Bayes' owner
owner = "DataCamp"
# Display variables
print(favorite_toy)
print(owner)# One or more of the following lines contains an error
# Correct it so that it runs without producing syntax errors
birthday = "2017-07-14"
case_id = 'DATACAMP!123-456?'# Import pandas
import pandas as pd
# Load the 'ransom.csv' into a DataFrame
r = pd.read_csv('ransom.csv')
# Display DataFrame
print(r)# One or more of the following lines contains an error
# Correct it so that it runs without producing syntax errors
# Plot a graph
plt.plot(x_values, y_values)
# Display the graph
plt.show()# Define plate to represent a plate beginning with FRQ
# Use * to represent the missing four letters
plate = 'FRQ****'
# Call the function lookup_plate()
lookup_plate(plate)
# Call lookup_plate() with the keyword argument for color
lookup_plate(plate, color = 'Green')
Loading Data in Pandas
# Import pandas under the alias pd
import pandas as pd# Load the CSV "credit_records.csv"
credit_records = pd.read_csv("credit_records.csv")# Display the first five rows of credit_records using the .head() method
print(credit_records.head())#Use .info() to inspect the DataFrame credit_records
print(credit_records.info())# Select the column item from credit_records
# Use brackets and string notation
items = credit_records['item']
# Display the results
print(items)# Select the column item from credit_records
# Use dot notation
items = credit_records.item
# Display the results
print(items)# One or more lines of code contain errors.
# Fix the errors so that the code runs.# Select the location column in credit_records
location = credit_records["location"]# Select the item column in credit_records
items = credit_records.item# Display results
print(location)# Use info() to inspect mpr
print(mpr.info())# The following code contains one or more errors
# Correct the mistakes in the code so that it runs without errors# Select column "Dog Name" from mpr
name = mpr['Dog Name']# Select column "Missing?" from mpr
is_missing = mpr['Missing?']# Display the columns
print(name)
print(is_missing)# Is height_inches greater than 70 inches?
print(height_inches > 70)# Is plate1 equal to "FRQ123"?
print(plate1 == "FRQ123")# Is fur_color not equal to "brown"?
print(fur_color != "brown")# Select the dogs where Age is greater than 2
greater_than_2 = mpr[mpr.Age > 2]
print(greater_than_2)# Select the dogs whose Status is equal to Still Missing
still_missing = mpr[mpr.Status == "Still Missing"]
print(still_missing)# Select all dogs whose Dog Breed is not equal to Poodle
not_poodle = mpr[mpr["Dog Breed"] != "Poodle"]
print(not_poodle)# Select purchases from 'Pet Paradise'
purchase = credit_records[credit_records.location == 'Pet Paradise']# Display
print(purchase)
Plotting Data with matplotlib
# From matplotlib, import pyplot under the alias plt
from matplotlib import pyplot as plt# Plot Officer Deshaun's hours_worked vs. day_of_week
plt.plot(deshaun.day_of_week, deshaun.hours_worked)# Display Deshaun's plot
plt.show()# Plot Officer Deshaun's hours_worked vs. day_of_week
plt.plot(deshaun.day_of_week, deshaun.hours_worked)# Plot Officer Aditya's hours_worked vs. day_of_week
plt.plot(aditya.day_of_week, aditya.hours_worked)# Plot Officer Mengfei's hours_worked vs. day_of_week
plt.plot(mengfei.day_of_week, mengfei.hours_worked)# Display all three line plots
plt.show()# Officer Deshaun
plt.plot(deshaun.day_of_week, deshaun.hours_worked, label='Deshaun')# Add a label to Aditya's plot
plt.plot(aditya.day_of_week, aditya.hours_worked, label='Aditya')# Add a label to Mengfei's plot
plt.plot(mengfei.day_of_week, mengfei.hours_worked, label='Mengfei')# Add a command to make the legend display
plt.legend()# Display plot
plt.show()# Lines
plt.plot(deshaun.day_of_week, deshaun.hours_worked, label='Deshaun')
plt.plot(aditya.day_of_week, aditya.hours_worked, label='Aditya')
plt.plot(mengfei.day_of_week, mengfei.hours_worked, label='Mengfei')# Add a title
plt.title("Officer Deshaun")# Add y-axis label
plt.ylabel("Hours Worked")# Legend
plt.legend()
# Display plot
plt.show()# Create plot
plt.plot(six_months.month, six_months.hours_worked)# Add annotation "Missing June data" at (2.5, 80)
plt.text(2.5,80, "Missing June data")# Display graph
plt.show()# Change the color of Phoenix to `"DarkCyan"`
plt.plot(data["Year"], data["Phoenix Police Dept"], label="Phoenix", color = "DarkCyan")# Make the Los Angeles line dotted
plt.plot(data["Year"], data["Los Angeles Police Dept"], label="Los Angeles", linestyle = ":")# Add square markers to Philedelphia
plt.plot(data["Year"], data["Philadelphia Police Dept"], label="Philadelphia", marker = "s")# Add a legend
plt.legend()# Display the plot
plt.show()# Change the style to fivethirtyeight
plt.style.use("fivethirtyeight")# Plot lines
plt.plot(data["Year"], data["Phoenix Police Dept"], label="Phoenix")
plt.plot(data["Year"], data["Los Angeles Police Dept"], label="Los Angeles")
plt.plot(data["Year"], data["Philadelphia Police Dept"], label="Philadelphia")# Add a legend
plt.legend()# Display the plot
plt.show()# Change the style to ggplot
plt.style.use("ggplot")# Plot lines
plt.plot(data["Year"], data["Phoenix Police Dept"], label="Phoenix")
plt.plot(data["Year"], data["Los Angeles Police Dept"], label="Los Angeles")
plt.plot(data["Year"], data["Philadelphia Police Dept"], label="Philadelphia")# Add a legend
plt.legend()# Display the plot
plt.show()# Choose any of the styles
plt.style.use('Solarize_Light2')# Plot lines
plt.plot(data["Year"], data["Phoenix Police Dept"], label="Phoenix")
plt.plot(data["Year"], data["Los Angeles Police Dept"], label="Los Angeles")
plt.plot(data["Year"], data["Philadelphia Police Dept"], label="Philadelphia")# Add a legend
plt.legend()# Display the plot
plt.show()# x should be ransom.letter and y should be ransom.frequency
plt.plot(ransom.letter, ransom.frequency,
# Label should be "Ransom"
label = "Ransom",
# Plot the ransom letter as a dotted gray line
linestyle = ':', color = 'gray')# Display the plot
plt.show()# Plot each line
plt.plot(ransom.letter, ransom.frequency,
label='Ransom', linestyle=':', color='gray')# X-values should be suspect1.letter
# Y-values should be suspect1.frequency
# Label should be "Fred Frequentist"
plt.plot(suspect1.letter, suspect1.frequency, label = "Fred Frequentist")# Display the plot
plt.show()# Plot each line
plt.plot(ransom.letter, ransom.frequency,
label='Ransom', linestyle=':', color='gray')
plt.plot(suspect1.letter, suspect1.frequency,
label='Fred Frequentist')# X-values should be suspect2.letter
# Y-values should be suspect2.frequency
# Label should be "Gertrude Cox"
plt.plot(suspect2.letter, suspect2.frequency, label = "Gertrude Cox")# Display plot
plt.show()# Plot each line
plt.plot(ransom.letter, ransom.frequency,
label='Ransom', linestyle=':', color='gray')
plt.plot(suspect1.letter, suspect1.frequency, label='Fred Frequentist')
plt.plot(suspect2.letter, suspect2.frequency, label='Gertrude Cox')# Add x- and y-labels
plt.xlabel("Letter")
plt.ylabel("Frequency")# Add a legend
plt.legend()# Display plot
plt.show()
Different Types of Plot
# Explore the data
print(cellphone.head())# Create a scatter plot of the data from the DataFrame cellphone
plt.scatter(cellphone.x, cellphone.y)# Add labels
plt.ylabel('Latitude')
plt.xlabel('Longitude')# Display the plot
plt.show()# Change the transparency to 0.1
plt.scatter(cellphone.x, cellphone.y,
color='red',
marker='s',
alpha=0.1)# Add labels
plt.ylabel('Latitude')
plt.xlabel('Longitude')# Display the plot
plt.show()# Display the DataFrame hours using print
print(hours)# Create a bar plot from the DataFrame hours
plt.bar(hours.officer, hours.avg_hours_worked,
# Add error bars
yerr = hours["std_hours_worked"])# Display the plot
plt.show()# Plot the number of hours spent on desk work
plt.bar(hours.officer, hours.desk_work, label='Desk Work')# Plot the hours spent on field work on top of desk work
plt.bar(hours.officer, hours.field_work, bottom = hours.desk_work, label = 'Field Work')# Add a legend
plt.legend()# Display the plot
plt.show()# Create a histogram of the column weight
# from the DataFrame puppies
plt.hist(puppies.weight)# Add labels
plt.xlabel('Puppy Weight (lbs)')
plt.ylabel('Number of Puppies')# Display
plt.show()# Change the number of bins to 50
plt.hist(puppies.weight,
bins = 50)# Add labels
plt.xlabel('Puppy Weight (lbs)')
plt.ylabel('Number of Puppies')# Display
plt.show()# Change the range to start at 5 and end at 35
plt.hist(puppies.weight,
range=(5, 35))# Add labels
plt.xlabel('Puppy Weight (lbs)')
plt.ylabel('Number of Puppies')# Display
plt.show()# Create a histogram of gravel.radius
plt.hist(gravel.radius)# Display histogram
plt.show()# Create a histogram
# Range is 2 to 8, with 40 bins
plt.hist(gravel.radius, bins = 40, range = (2,8))# Display histogram
plt.show()# Create a histogram
# Normalize to 1
plt.hist(gravel.radius,
bins=40,
range=(2, 8),
density=True)# Display histogram
plt.show()# Create a histogram
plt.hist(gravel.radius,
bins=40,
range=(2, 8),
density=True)# Label plot
plt.xlabel('Gravel Radius (mm)')
plt.ylabel('Frequency')
plt.title('Sample from Shoeprint')# Display histogram
plt.show()
Codes ini bersumber dari hasil belajar di DataCamp. Link DataCamp: https://www.datacamp.com.
My Statement of Accomplishment for this chapter:
Terima kasih telah membaca.
