Introduction to Data Science in Python

Introduction to Data Science in Python

Run the hidden code cell below to import the data used in this course.

Take Notes

Add notes about the concepts you've learned and code cells with code you want to keep.

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())

Select the location column in credit_records

location = credit_records['location']

Select the item column in credit_records

items = credit_records.item

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)

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()

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()

Add a title

plt.title("descriptive")

Add y-axis label

plt.ylabel("hours worked")

Add annotation "Missing June data" at (2.5, 80)

plt.text(2.5, 80, "Missing June data")

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")

Change the style to fivethirtyeight

plt.style.use("fivethirtyeight")

Create a scatter plot of the data from the DataFrame cellphone

plt.scatter(cellphone.x, cellphone.y)

Create a bar plot from the DataFrame hours

plt.bar(hours.officer, hours.avg_hours_worked, # Add error bars yerr=hours.std_hours_worked)

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')

Create a histogram of the column weight from the DataFrame puppies

plt.hist(puppies.weight)

Create a histogram

Range is 2 to 8, with 40 bins

plt.hist(gravel.radius, range=(2,8), bins=40)

Create a histogram

Normalize to 1

plt.hist(gravel.radius, bins=40, range=(2, 8), density = 1)

my_house greater than 18.5 or smaller than 10

print(np.logical_or(my_house>18.5, my_house<10))

Both my_house and your_house smaller than 11

print(np.logical_and(my_house<11, your_house<11))