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Data Manipulation with pandas
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• ## .mfe-app-workspace-kj242g{position:absolute;top:-8px;}.mfe-app-workspace-11ezf91{display:inline-block;}.mfe-app-workspace-11ezf91:hover .Anchor__copyLink{visibility:visible;}Data Manipulation with pandas

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

```.mfe-app-workspace-11z5vno{font-family:JetBrainsMonoNL,Menlo,Monaco,'Courier New',monospace;font-size:13px;line-height:20px;}```# Import the course packages
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

# Import the four datasets

### Take Notes

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

``````#DATA MANIPULATION WITH PANDAS
"Introduction to pandas"
#Exploring a DataFrame

#Chapter 1: DataFrames
"Sorting and subsetting"
"Creating new columns"

#Chapter 2: Aggregating Data
'Summary statistics'
'Counting'
'Grouped summary statistics'

#Chapter 3: Slicing and Indexing Data
'Subsetting using slicing'
'Indexes and subsetting using indexes'

#Chapter 4: Creating and Visualizing Data
'Plotting'
'Handling missing data'

#Exploring a DataFrame:

# the first few rows (the “head” of the DataFrame)
dogs.info()
# shows information on each of the columns, such as the data type and number of missing values
dogs.shape
# returns the number of rows and columns of the DataFrame
dogs.describe()
# calculates a few summary statistics for each column
dogs.values
# A two-dimensional NumPy array of values.
dogs.columns
# An index of columns: the column names.
dogs.index
# An index for the rows: either row numbers or row names.

#Chapter 1: DataFrames
'Sorting and subsetting'

dogs.sort_values("weight_kg")
#Sort the lightest dog at the top
dogs.sort_values("weight_kg", ascending=False)
#Sort the heaviest dog at the top
dogs.sort_values(["weight_kg","height_cm"])
#Sort the lightest dog at the top Then the shortest dog
dogs.sort_values(["weight_kg","height_cm"],ascending=[True,False])
#Sort the lightest dog at the top Then the tallest dog

"Subsetting Coulmns"

dogs["name"]
#Subsetting DataFrame[“Coulmn_name”]
dogs[["name","weight_kg"]]
#Subsetting multiple columns

"Subsetting Rows"

Dogs["height_cm"] > 50
#Get True or false values

dogs[dogs["height_cm"] > 50]
#Get The rows of dogs taller than 50

#Subsetting based on text data

dogs[dogs["date_of_birth"] < "2015-01-01"]
#Subsetting based on dates

is_brown= dogs["color"] == "Brown"
dogs[is_lab & is_brown]
#Subsetting based on multiple conditions

Is_balck_or_brown = dogs["color"].isin(["Black","Brown"])
dogs[is_balck_or_brown]
#Subsetting based on multiple conditions using .isin()

"Creating a new column"

dogs["height_m"]=dogs["height_cm"]/100
Hidden output

``# Add your code snippets here``
``````# Import the course packages
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

# Import the four datasets

# Add total col as sum of individuals and family_members
homelessness["total"] = homelessness["individuals"]+homelessness["family_members"]

# Add p_individuals col as proportion of total that are individuals
homelessness["p_individuals"]=homelessness["individuals"]/homelessness["total"]

# See the result
print(homelessness)``````

### Explore Datasets

Use the DataFrames imported in the first cell to explore the data and practice your skills!

• Print the highest weekly sales for each `department` in the `walmart` DataFrame. Limit your results to the top five departments, in descending order. If you're stuck, try reviewing this video.
• What was the total `nb_sold` of organic avocados in 2017 in the `avocado` DataFrame? If you're stuck, try reviewing this video.
• Create a bar plot of the total number of homeless people by region in the `homelessness` DataFrame. Order the bars in descending order. Bonus: create a horizontal bar chart. If you're stuck, try reviewing this video.
• Create a line plot with two lines representing the temperatures in Toronto and Rome. Make sure to properly label your plot. Bonus: add a legend for the two lines. If you're stuck, try reviewing this video.