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A Quick Introduction to Data Analysis and Manipulation with Python and pandas¶

In [1]:

import datetime
print(f"Last updated: {datetime.datetime.now()}")

import datetime print(f"Last updated: {datetime.datetime.now()}")

Last updated: 2024-09-04 16:09:35.139163

What is pandas?¶

If you're getting into machine learning and data science and you're using Python, you're going to use pandas.

pandas is an open source library which helps you analyse and manipulate data.

Why pandas?¶

pandas provides a simple to use but very capable set of functions you can use to on your data.

It's integrated with many other data science and machine learning tools which use Python so having an understanding of it will be helpful throughout your journey.

One of the main use cases you'll come across is using pandas to transform your data in a way which makes it usable with machine learning algorithms.

What does this notebook cover?¶

Because the pandas library is vast, there's often many ways to do the same thing. This notebook covers some of the most fundamental functions of the library, which are more than enough to get started.

Where can I get help?¶

If you get stuck or think of something you'd like to do which this notebook doesn't cover, don't fear!

The recommended steps you take are:

1. Try it - Since pandas is very friendly, your first step should be to use what you know and try figure out the answer to your own question (getting it wrong is part of the process). If in doubt, run your code.
2. Search for it - If trying it on your own doesn't work, since someone else has probably tried to do something similar, try searching for your problem in the following places (either via a search engine or direct):
   • pandas documentation - the best place for learning pandas, this resource covers all of the pandas functionality.
   • Stack Overflow - this is the developers Q&A hub, it's full of questions and answers of different problems across a wide range of software development topics and chances are, there's one related to your problem.
   • ChatGPT - ChatGPT is very good at explaining code, however, it can make mistakes. Best to verify the code it writes first before using it. Try asking "Can you explain the following code for me? {your code here}" and then continue with follow up questions from there.

An example of searching for a pandas function might be:

"how to fill all the missing values of two columns using pandas"

Searching this on Google leads to this post on Stack Overflow: https://stackoverflow.com/questions/36556256/how-do-i-fill-na-values-in-multiple-columns-in-pandas

The next steps here are to read through the post and see if it relates to your problem. If it does, great, take the code/information you need and rewrite it to suit your own problem.

3. Ask for help - If you've been through the above 2 steps and you're still stuck, you might want to ask your question on Stack Overflow. Remember to be specific as possible and provide details on what you've tried.

Remember, you don't have to learn all of these functions off by heart to begin with.

What's most important is remembering to continually ask yourself, "what am I trying to do with the data?".

Start by answering that question and then practicing finding the code which does it.

Let's get started.

0. Importing pandas¶

To get started using pandas, the first step is to import it.

The most common way (and method you should use) is to import pandas as the abbreviation pd (e.g. pandas -> pd).

If you see the letters pd used anywhere in machine learning or data science, it's probably referring to the pandas library.

In [2]:

import pandas as pd

# Print the version
print(f"pandas version: {pd.__version__}")

import pandas as pd # Print the version print(f"pandas version: {pd.__version__}")

pandas version: 2.2.2

1. Datatypes¶

pandas has two main datatypes, Series and DataFrame.

• pandas.Series - a 1-dimensional column of data.
• pandas.DataFrame (most common) - a 2-dimesional table of data with rows and columns.

You can create a Series using pd.Series() and passing it a Python list.

In [3]:

# Creating a series of car types
cars = pd.Series(["BMW", "Toyota", "Honda"])
cars

# Creating a series of car types cars = pd.Series(["BMW", "Toyota", "Honda"]) cars

Out[3]:

0       BMW
1    Toyota
2     Honda
dtype: object

In [4]:

# Creating a series of colours
colours = pd.Series(["Blue", "Red", "White"])
colours

# Creating a series of colours colours = pd.Series(["Blue", "Red", "White"]) colours

Out[4]:

0     Blue
1      Red
2    White
dtype: object

You can create a DataFrame by using pd.DataFrame() and passing it a Python dictionary.

Let's use our two Series as the values.

In [5]:

# Creating a DataFrame of cars and colours
car_data = pd.DataFrame({"Car type": cars, 
                         "Colour": colours})
car_data

# Creating a DataFrame of cars and colours car_data = pd.DataFrame({"Car type": cars, "Colour": colours}) car_data

Out[5]:

	Car type	Colour
0	BMW	Blue
1	Toyota	Red
2	Honda	White

You can see the keys of the dictionary became the column headings (text in bold) and the values of the two Series's became the values in the DataFrame.

It's important to note, many different types of data could go into the DataFrame.

Here we've used only text but you could use floats, integers, dates and more.

Exercises¶

1. Make a Series of different foods.
2. Make a Series of different dollar values (these can be integers).
3. Combine your Series's of foods and dollar values into a DataFrame.

Try it out for yourself first, then see how your code goes against the solution.

Note: Make sure your two Series are the same size before combining them in a DataFrame.

In [6]:

# Your code here

# Your code here

In [7]:

# Example solution

# Make a Series of different foods
foods = pd.Series(["Almond butter", "Eggs", "Avocado"])

# Make a Series of different dollar values 
prices = pd.Series([9, 6, 2])

# Combine your Series of foods and dollar values into a DataFrame
food_data = pd.DataFrame({"Foods": foods,
                          "Price": prices})

food_data

# Example solution # Make a Series of different foods foods = pd.Series(["Almond butter", "Eggs", "Avocado"]) # Make a Series of different dollar values prices = pd.Series([9, 6, 2]) # Combine your Series of foods and dollar values into a DataFrame food_data = pd.DataFrame({"Foods": foods, "Price": prices}) food_data

Out[7]:

	Foods	Price
0	Almond butter	9
1	Eggs	6
2	Avocado	2

2. Importing data¶

Creating Series and DataFrame's from scratch is nice but what you'll usually be doing is importing your data in the form of a .csv (comma separated value), spreadsheet file or something similar such as an SQL database.

pandas allows for easy importing of data like this through functions such as pd.read_csv() and pd.read_excel() (for Microsoft Excel files).

Say you wanted to get this information from this Google Sheet document into a pandas DataFrame.

You could export it as a .csv file and then import it using pd.read_csv().

Tip: If the Google Sheet is public, pd.read_csv() can read it via URL, try searching for "pandas read Google Sheet with URL".

In this case, the exported .csv file is called car-sales.csv.

In [8]:

# Import car sales data
car_sales = pd.read_csv("../data/car-sales.csv") # takes a filename as string as input

# Option 2: Read directly from a URL/Google Sheets
# If you are reading from GitHub, be sure to use the "raw" link (original link: https://github.com/mrdbourke/zero-to-mastery-ml/blob/master/data/car-sales.csv)
car_sales = pd.read_csv("https://raw.githubusercontent.com/mrdbourke/zero-to-mastery-ml/master/data/car-sales.csv")
car_sales

# Import car sales data car_sales = pd.read_csv("../data/car-sales.csv") # takes a filename as string as input # Option 2: Read directly from a URL/Google Sheets # If you are reading from GitHub, be sure to use the "raw" link (original link: https://github.com/mrdbourke/zero-to-mastery-ml/blob/master/data/car-sales.csv) car_sales = pd.read_csv("https://raw.githubusercontent.com/mrdbourke/zero-to-mastery-ml/master/data/car-sales.csv") car_sales

Out[8]:

	Make	Colour	Odometer (KM)	Doors	Price
0	Toyota	White	150043	4	$4,000.00
1	Honda	Red	87899	4	$5,000.00
2	Toyota	Blue	32549	3	$7,000.00
3	BMW	Black	11179	5	$22,000.00
4	Nissan	White	213095	4	$3,500.00
5	Toyota	Green	99213	4	$4,500.00
6	Honda	Blue	45698	4	$7,500.00
7	Honda	Blue	54738	4	$7,000.00
8	Toyota	White	60000	4	$6,250.00
9	Nissan	White	31600	4	$9,700.00

Now we've got the same data from the spreadsheet available in a pandas DataFrame called car_sales.

Having your data available in a DataFrame allows you to take advantage of all of pandas functionality on it.

Another common practice you'll see is data being imported to DataFrame called df (short for DataFrame).

In [9]:

# Import the car sales data and save it to df

# Option 1: Read from a CSV file (stored on our local computer)
df = pd.read_csv("../data/car-sales.csv")

# Option 2: Read directly from a URL/Google Sheets (if the file is hosted online)
df = pd.read_csv("https://raw.githubusercontent.com/mrdbourke/zero-to-mastery-ml/master/data/car-sales.csv")
df

# Import the car sales data and save it to df # Option 1: Read from a CSV file (stored on our local computer) df = pd.read_csv("../data/car-sales.csv") # Option 2: Read directly from a URL/Google Sheets (if the file is hosted online) df = pd.read_csv("https://raw.githubusercontent.com/mrdbourke/zero-to-mastery-ml/master/data/car-sales.csv") df

Out[9]:

	Make	Colour	Odometer (KM)	Doors	Price
0	Toyota	White	150043	4	$4,000.00
1	Honda	Red	87899	4	$5,000.00
2	Toyota	Blue	32549	3	$7,000.00
3	BMW	Black	11179	5	$22,000.00
4	Nissan	White	213095	4	$3,500.00
5	Toyota	Green	99213	4	$4,500.00
6	Honda	Blue	45698	4	$7,500.00
7	Honda	Blue	54738	4	$7,000.00
8	Toyota	White	60000	4	$6,250.00
9	Nissan	White	31600	4	$9,700.00

Now car_sales and df contain the exact same information, the only difference is the name. Like any other variable, you can name your DataFrame's whatever you want. But best to choose something simple.

Anatomy of a DataFrame¶

Different functions use different labels for different things. This graphic sums up some of the main components of DataFrame's and their different names.

3. Exporting data¶

After you've made a few changes to your data, you might want to export it and save it so someone else can access the changes.

pandas allows you to export DataFrame's to .csv format using .to_csv() or spreadsheet format using .to_excel().

We haven't made any changes yet to the car_sales DataFrame but let's try export it.

In [10]:

# Export the car sales DataFrame to csv
car_sales.to_csv("../data/exported-car-sales.csv")

# Export the car sales DataFrame to csv car_sales.to_csv("../data/exported-car-sales.csv")

Running this will save a file called export-car-sales.csv to the current folder.

Exercises¶

1. Practice importing a .csv file using pd.read_csv(), you can download heart-disease.csv. This file contains annonymous patient medical records and whether or not they have heart disease.
2. Practice exporting a DataFrame using .to_csv(). You could export the heart disease DataFrame after you've imported it.

Note:

• Make sure the heart-disease.csv file is in the same folder as your notebook orbe sure to use the filepath where the file is.
• You can name the variables and exported files whatever you like but make sure they're readable.

In [11]:

# Your code here

# Your code here

Example solution¶

In [12]:

# Importing heart-disease.csv
patient_data = pd.read_csv("../data/heart-disease.csv")
patient_data

# Importing heart-disease.csv patient_data = pd.read_csv("../data/heart-disease.csv") patient_data

Out[12]:

	age	sex	cp	trestbps	chol	fbs	restecg	thalach	exang	oldpeak	slope	ca	thal	target
0	63	1	3	145	233	1	0	150	0	2.3	0	0	1	1
1	37	1	2	130	250	0	1	187	0	3.5	0	0	2	1
2	41	0	1	130	204	0	0	172	0	1.4	2	0	2	1
3	56	1	1	120	236	0	1	178	0	0.8	2	0	2	1
4	57	0	0	120	354	0	1	163	1	0.6	2	0	2	1
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
298	57	0	0	140	241	0	1	123	1	0.2	1	0	3	0
299	45	1	3	110	264	0	1	132	0	1.2	1	0	3	0
300	68	1	0	144	193	1	1	141	0	3.4	1	2	3	0
301	57	1	0	130	131	0	1	115	1	1.2	1	1	3	0
302	57	0	1	130	236	0	0	174	0	0.0	1	1	2	0

303 rows × 14 columns

In [13]:

# Exporting the patient_data DataFrame to csv
patient_data.to_csv("../data/exported-patient-data.csv")

# Exporting the patient_data DataFrame to csv patient_data.to_csv("../data/exported-patient-data.csv")

4. Describing data¶

One of the first things you'll want to do after you import some data into a pandas DataFrame is to start exploring it.

pandas has many built in functions which allow you to quickly get information about a DataFrame.

Let's explore some using the car_sales DataFrame.

In [14]:

car_sales

car_sales

Out[14]:

	Make	Colour	Odometer (KM)	Doors	Price
0	Toyota	White	150043	4	$4,000.00
1	Honda	Red	87899	4	$5,000.00
2	Toyota	Blue	32549	3	$7,000.00
3	BMW	Black	11179	5	$22,000.00
4	Nissan	White	213095	4	$3,500.00
5	Toyota	Green	99213	4	$4,500.00
6	Honda	Blue	45698	4	$7,500.00
7	Honda	Blue	54738	4	$7,000.00
8	Toyota	White	60000	4	$6,250.00
9	Nissan	White	31600	4	$9,700.00

.dtypes shows us what datatype each column contains.

In [15]:

car_sales.dtypes

car_sales.dtypes

Out[15]:

Make             object
Colour           object
Odometer (KM)     int64
Doors             int64
Price            object
dtype: object

Notice how the Price column isn't an integer like Odometer or Doors. Don't worry, pandas makes this easy to fix.

.describe() gives you a quick statistical overview of the numerical columns.

In [16]:

car_sales.describe()

car_sales.describe()

Out[16]:

	Odometer (KM)	Doors
count	10.000000	10.000000
mean	78601.400000	4.000000
std	61983.471735	0.471405
min	11179.000000	3.000000
25%	35836.250000	4.000000
50%	57369.000000	4.000000
75%	96384.500000	4.000000
max	213095.000000	5.000000

.info() shows a handful of useful information about a DataFrame such as:

• How many entries (rows) there are
• Whether there are missing values (if a columns non-null value is less than the number of entries, it has missing values)
• The datatypes of each column

In [17]:

car_sales.info()

car_sales.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 10 entries, 0 to 9
Data columns (total 5 columns):
 #   Column         Non-Null Count  Dtype 
---  ------         --------------  ----- 
 0   Make           10 non-null     object
 1   Colour         10 non-null     object
 2   Odometer (KM)  10 non-null     int64 
 3   Doors          10 non-null     int64 
 4   Price          10 non-null     object
dtypes: int64(2), object(3)
memory usage: 532.0+ bytes

You can also call various statistical and mathematical methods such as .mean() or .sum() directly on a DataFrame or Series.

In [18]:

# Calling .mean() on a DataFrame
car_sales.mean(numeric_only=True) # numeric_only = get mean values of numeric columnns only

# Calling .mean() on a DataFrame car_sales.mean(numeric_only=True) # numeric_only = get mean values of numeric columnns only

Out[18]:

Odometer (KM)    78601.4
Doors                4.0
dtype: float64

In [19]:

# Calling .mean() on a Series
car_prices = pd.Series([3000, 3500, 11250])
car_prices.mean()

# Calling .mean() on a Series car_prices = pd.Series([3000, 3500, 11250]) car_prices.mean()

Out[19]:

np.float64(5916.666666666667)

In [20]:

# Calling .sum() on a DataFrame with numeric_only=False (default)
car_sales.sum(numeric_only=False)

# Calling .sum() on a DataFrame with numeric_only=False (default) car_sales.sum(numeric_only=False)

Out[20]:

Make             ToyotaHondaToyotaBMWNissanToyotaHondaHondaToyo...
Colour               WhiteRedBlueBlackWhiteGreenBlueBlueWhiteWhite
Odometer (KM)                                               786014
Doors                                                           40
Price            $4,000.00$5,000.00$7,000.00$22,000.00$3,500.00...
dtype: object

In [21]:

# Calling .sum() on a DataFrame with numeric_only=True
car_sales.sum(numeric_only=True)

# Calling .sum() on a DataFrame with numeric_only=True car_sales.sum(numeric_only=True)

Out[21]:

Odometer (KM)    786014
Doors                40
dtype: int64

In [22]:

# Calling .sum() on a Series
car_prices.sum()

# Calling .sum() on a Series car_prices.sum()

Out[22]:

np.int64(17750)

Calling these on a whole DataFrame may not be as helpful as targeting an individual column. But it's helpful to know they're there.

.columns will show you all the columns of a DataFrame.

In [23]:

car_sales.columns

car_sales.columns

Out[23]:

Index(['Make', 'Colour', 'Odometer (KM)', 'Doors', 'Price'], dtype='object')

You can save them to a list which you could use later.

In [24]:

# Save car_sales columns to a list 
car_columns = car_sales.columns
car_columns[0]

# Save car_sales columns to a list car_columns = car_sales.columns car_columns[0]

Out[24]:

'Make'

.index will show you the values in a DataFrame's index (the column on the far left).

In [25]:

car_sales.index

car_sales.index

Out[25]:

RangeIndex(start=0, stop=10, step=1)

pandas DataFrame's, like Python lists, are 0-indexed (unless otherwise changed). This means they start at 0.

In [26]:

# Show the length of a DataFrame
len(car_sales)

# Show the length of a DataFrame len(car_sales)

Out[26]:

10

So even though the length of our car_sales dataframe is 10, this means the indexes go from 0-9.

5. Viewing and selecting data¶

Some common methods for viewing and selecting data in a pandas DataFrame include:

• DataFrame.head(n=5) - Displays the first n rows of a DataFrame (e.g. car_sales.head() will show the first 5 rows of the car_sales DataFrame).
• DataFrame.tail(n=5) - Displays the last n rows of a DataFrame.
• DataFrame.loc[] - Accesses a group of rows and columns by labels or a boolean array.
• DataFrame.iloc[] - Accesses a group of rows and columns by integer indices (e.g. car_sales.iloc[0] shows all the columns from index 0.
• DataFrame.columns - Lists the column labels of the DataFrame.
• DataFrame['A'] - Selects the column named 'A' from the DataFrame.
• DataFrame[DataFrame['A'] > 5] - Boolean indexing filters rows based on column values meeting a condition (e.g. all rows from column 'A' greater than 5.
• DataFrame.plot() - Creates a line plot of a DataFrame's columns (e.g. plot Make vs. Odometer (KM) columns with car_sales[["Make", "Odometer (KM)"]].plot();).
• DataFrame.hist() - Generates histograms for columns in a DataFrame.
• pandas.crosstab() - Computes a cross-tabulation of two or more factors.

In practice, you'll constantly be making changes to your data, and viewing it. Changing it, viewing it, changing it, viewing it.

You won't always want to change all of the data in your DataFrame's either. So there are just as many different ways to select data as there is to view it.

.head() allows you to view the first 5 rows of your DataFrame. You'll likely be using this one a lot.

In [27]:

# Show the first 5 rows of car_sales
car_sales.head()

# Show the first 5 rows of car_sales car_sales.head()

Out[27]:

	Make	Colour	Odometer (KM)	Doors	Price
0	Toyota	White	150043	4	$4,000.00
1	Honda	Red	87899	4	$5,000.00
2	Toyota	Blue	32549	3	$7,000.00
3	BMW	Black	11179	5	$22,000.00
4	Nissan	White	213095	4	$3,500.00

Why 5 rows? Good question. I don't know the answer. But 5 seems like a good amount.

Want more than 5?

No worries, you can pass .head() an integer to display more than or less than 5 rows.

In [28]:

# Show the first 7 rows of car_sales
car_sales.head(7)

# Show the first 7 rows of car_sales car_sales.head(7)

Out[28]:

	Make	Colour	Odometer (KM)	Doors	Price
0	Toyota	White	150043	4	$4,000.00
1	Honda	Red	87899	4	$5,000.00
2	Toyota	Blue	32549	3	$7,000.00
3	BMW	Black	11179	5	$22,000.00
4	Nissan	White	213095	4	$3,500.00
5	Toyota	Green	99213	4	$4,500.00
6	Honda	Blue	45698	4	$7,500.00

.tail() allows you to see the bottom 5 rows of your DataFrame. This is helpful if your changes are influencing the bottom rows of your data.

In [29]:

# Show bottom 5 rows of car_sales
car_sales.tail()

# Show bottom 5 rows of car_sales car_sales.tail()

Out[29]:

	Make	Colour	Odometer (KM)	Doors	Price
5	Toyota	Green	99213	4	$4,500.00
6	Honda	Blue	45698	4	$7,500.00
7	Honda	Blue	54738	4	$7,000.00
8	Toyota	White	60000	4	$6,250.00
9	Nissan	White	31600	4	$9,700.00

You can use .loc[] and .iloc[] to select data from your Series and DataFrame's.

Let's see.

In [30]:

# Create a sample series
animals = pd.Series(["cat", "dog", "bird", "snake", "ox", "lion"], 
                    index=[0, 3, 9, 8, 67, 3])
animals

# Create a sample series animals = pd.Series(["cat", "dog", "bird", "snake", "ox", "lion"], index=[0, 3, 9, 8, 67, 3]) animals

Out[30]:

0       cat
3       dog
9      bird
8     snake
67       ox
3      lion
dtype: object

.loc[] takes an integer or label as input. And it chooses from your Series or DataFrame whichever index matches the number.

In [31]:

# Select all indexes with 3
animals.loc[3]

# Select all indexes with 3 animals.loc[3]

Out[31]:

3     dog
3    lion
dtype: object

In [32]:

# Select index 9
animals.loc[9]

# Select index 9 animals.loc[9]

Out[32]:

'bird'

Let's try with our car_sales DataFrame.

In [33]:

car_sales

car_sales

Out[33]:

	Make	Colour	Odometer (KM)	Doors	Price
0	Toyota	White	150043	4	$4,000.00
1	Honda	Red	87899	4	$5,000.00
2	Toyota	Blue	32549	3	$7,000.00
3	BMW	Black	11179	5	$22,000.00
4	Nissan	White	213095	4	$3,500.00
5	Toyota	Green	99213	4	$4,500.00
6	Honda	Blue	45698	4	$7,500.00
7	Honda	Blue	54738	4	$7,000.00
8	Toyota	White	60000	4	$6,250.00
9	Nissan	White	31600	4	$9,700.00

In [34]:

# Select row at index 3
car_sales.loc[3]

# Select row at index 3 car_sales.loc[3]

Out[34]:

Make                    BMW
Colour                Black
Odometer (KM)         11179
Doors                     5
Price            $22,000.00
Name: 3, dtype: object

iloc[] does a similar thing but works with exact positions.

In [35]:

animals

animals

Out[35]:

0       cat
3       dog
9      bird
8     snake
67       ox
3      lion
dtype: object

In [36]:

# Select row at position 3
animals.iloc[3]

# Select row at position 3 animals.iloc[3]

Out[36]:

'snake'

Even though 'snake' appears at index 8 in the series, it's shown using .iloc[3] because it's at the 3rd (starting from 0) position.

Let's try with the car_sales DataFrame.

In [37]:

# Select row at position 3
car_sales.iloc[3]

# Select row at position 3 car_sales.iloc[3]

Out[37]:

Make                    BMW
Colour                Black
Odometer (KM)         11179
Doors                     5
Price            $22,000.00
Name: 3, dtype: object

You can see it's the same as .loc[] because the index is in order, position 3 is the same as index 3.

You can also use slicing with .loc[] and .iloc[].

In [38]:

# Get all rows up to position 3
animals.iloc[:3]

# Get all rows up to position 3 animals.iloc[:3]

Out[38]:

0     cat
3     dog
9    bird
dtype: object

In [39]:

# Get all rows up to (and including) index 3
car_sales.loc[:3]

# Get all rows up to (and including) index 3 car_sales.loc[:3]

Out[39]:

	Make	Colour	Odometer (KM)	Doors	Price
0	Toyota	White	150043	4	$4,000.00
1	Honda	Red	87899	4	$5,000.00
2	Toyota	Blue	32549	3	$7,000.00
3	BMW	Black	11179	5	$22,000.00

In [40]:

# Get all rows of the "Colour" column
car_sales.loc[:, "Colour"] # note: ":" stands for "all", e.g. "all indices in the first axis"

# Get all rows of the "Colour" column car_sales.loc[:, "Colour"] # note: ":" stands for "all", e.g. "all indices in the first axis"

Out[40]:

0    White
1      Red
2     Blue
3    Black
4    White
5    Green
6     Blue
7     Blue
8    White
9    White
Name: Colour, dtype: object

When should you use .loc[] or .iloc[]?

• Use .loc[] when you're selecting rows and columns based on their lables or a condition (e.g. retrieving data for specific columns).
• Use .iloc[] when you're selecting rows and columns based on their integer index positions (e.g. extracting the first ten rows regardless of the labels).

However, in saying this, it will often take a bit of practice with each of the methods before you figure out which you'd like to use.

If you want to select a particular column, you can use DataFrame.['COLUMN_NAME'].

In [41]:

# Select Make column
car_sales['Make']

# Select Make column car_sales['Make']

Out[41]:

0    Toyota
1     Honda
2    Toyota
3       BMW
4    Nissan
5    Toyota
6     Honda
7     Honda
8    Toyota
9    Nissan
Name: Make, dtype: object

In [42]:

# Select Colour column
car_sales['Colour']

# Select Colour column car_sales['Colour']

Out[42]:

0    White
1      Red
2     Blue
3    Black
4    White
5    Green
6     Blue
7     Blue
8    White
9    White
Name: Colour, dtype: object

Boolean indexing works with column selection too. Using it will select the rows which fulfill the condition in the brackets.

In [43]:

# Select cars with over 100,000 on the Odometer
car_sales[car_sales["Odometer (KM)"] > 100000]

# Select cars with over 100,000 on the Odometer car_sales[car_sales["Odometer (KM)"] > 100000]

Out[43]:

	Make	Colour	Odometer (KM)	Doors	Price
0	Toyota	White	150043	4	$4,000.00
4	Nissan	White	213095	4	$3,500.00

In [44]:

# Select cars which are made by Toyota
car_sales[car_sales["Make"] == "Toyota"]

# Select cars which are made by Toyota car_sales[car_sales["Make"] == "Toyota"]

Out[44]:

	Make	Colour	Odometer (KM)	Doors	Price
0	Toyota	White	150043	4	$4,000.00
2	Toyota	Blue	32549	3	$7,000.00
5	Toyota	Green	99213	4	$4,500.00
8	Toyota	White	60000	4	$6,250.00

pd.crosstab() is a great way to view two different columns together and compare them.

In [45]:

# Compare car Make with number of Doors
pd.crosstab(car_sales["Make"], car_sales["Doors"])

# Compare car Make with number of Doors pd.crosstab(car_sales["Make"], car_sales["Doors"])

Out[45]:

Doors	3	4	5
Make
BMW	0	0	1
Honda	0	3	0
Nissan	0	2	0
Toyota	1	3	0

If you want to compare more columns in the context of another column, you can use .groupby().

In [46]:

car_sales

car_sales

Out[46]:

	Make	Colour	Odometer (KM)	Doors	Price
0	Toyota	White	150043	4	$4,000.00
1	Honda	Red	87899	4	$5,000.00
2	Toyota	Blue	32549	3	$7,000.00
3	BMW	Black	11179	5	$22,000.00
4	Nissan	White	213095	4	$3,500.00
5	Toyota	Green	99213	4	$4,500.00
6	Honda	Blue	45698	4	$7,500.00
7	Honda	Blue	54738	4	$7,000.00
8	Toyota	White	60000	4	$6,250.00
9	Nissan	White	31600	4	$9,700.00

In [47]:

# Group by the Make column and find the mean of the other columns 
car_sales.groupby(["Make"]).mean(numeric_only=True)

# Group by the Make column and find the mean of the other columns car_sales.groupby(["Make"]).mean(numeric_only=True)

Out[47]:

	Odometer (KM)	Doors
Make
BMW	11179.000000	5.00
Honda	62778.333333	4.00
Nissan	122347.500000	4.00
Toyota	85451.250000	3.75

pandas even allows for quick plotting of columns so you can see your data visualling. To plot, you'll have to import matplotlib. If your plots aren't showing, try running the two lines of code below.

%matplotlib inline is a special command which tells Jupyter to show your plots. Commands with % at the front are called magic commands.

In [52]:

# Import matplotlib and tell Jupyter to show plots
import matplotlib.pyplot as plt
%matplotlib inline

# Import matplotlib and tell Jupyter to show plots import matplotlib.pyplot as plt %matplotlib inline

You can visualize a column by calling .plot() on it.

In [53]:

car_sales["Odometer (KM)"].plot(); # tip: the ";" on the end prevents matplotlib from outputing the plot class

car_sales["Odometer (KM)"].plot(); # tip: the ";" on the end prevents matplotlib from outputing the plot class

Or compare two columns by passing them as x and y to plot().

In [54]:

car_sales.plot(x="Make", y="Odometer (KM)");

car_sales.plot(x="Make", y="Odometer (KM)");

You can see the distribution of a column by calling .hist() on you.

The distribution of something is a way of describing the spread of different values.

In [55]:

car_sales["Odometer (KM)"].hist()

car_sales["Odometer (KM)"].hist()

Out[55]:

<Axes: >

In this case, the majority of the distribution (spread) of the "Odometer (KM)" column is more towards the left of the graph. And there are two values which are more to the right. These two values to the right could be considered outliers (not part of the majority).

Now what if we wanted to plot our "Price" column?

Let's try.

In [56]:

car_sales["Price"].plot()

car_sales["Price"].plot()

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
Cell In[56], line 1
----> 1 car_sales["Price"].plot()

File ~/miniforge3/envs/ai/lib/python3.11/site-packages/pandas/plotting/_core.py:1030, in PlotAccessor.__call__(self, *args, **kwargs)
   1027             label_name = label_kw or data.columns
   1028             data.columns = label_name
-> 1030 return plot_backend.plot(data, kind=kind, **kwargs)

File ~/miniforge3/envs/ai/lib/python3.11/site-packages/pandas/plotting/_matplotlib/__init__.py:71, in plot(data, kind, **kwargs)
     69         kwargs["ax"] = getattr(ax, "left_ax", ax)
     70 plot_obj = PLOT_CLASSES[kind](data, **kwargs)
---> 71 plot_obj.generate()
     72 plot_obj.draw()
     73 return plot_obj.result

File ~/miniforge3/envs/ai/lib/python3.11/site-packages/pandas/plotting/_matplotlib/core.py:499, in MPLPlot.generate(self)
    497 @final
    498 def generate(self) -> None:
--> 499     self._compute_plot_data()
    500     fig = self.fig
    501     self._make_plot(fig)

File ~/miniforge3/envs/ai/lib/python3.11/site-packages/pandas/plotting/_matplotlib/core.py:698, in MPLPlot._compute_plot_data(self)
    696 # no non-numeric frames or series allowed
    697 if is_empty:
--> 698     raise TypeError("no numeric data to plot")
    700 self.data = numeric_data.apply(type(self)._convert_to_ndarray)

TypeError: no numeric data to plot

Trying to run it leaves us with an error. This is because the "Price" column of car_sales isn't in numeric form. We can tell this because of the TypeError: no numeric data to plot at the bottom of the cell.

We can check this with .info().

In [57]:

car_sales.info()

car_sales.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 10 entries, 0 to 9
Data columns (total 5 columns):
 #   Column         Non-Null Count  Dtype 
---  ------         --------------  ----- 
 0   Make           10 non-null     object
 1   Colour         10 non-null     object
 2   Odometer (KM)  10 non-null     int64 
 3   Doors          10 non-null     int64 
 4   Price          10 non-null     object
dtypes: int64(2), object(3)
memory usage: 532.0+ bytes

So what can we do?

We need to convert the "Price" column to a numeric type.

How?

We could try a few different things on our own. But let's practice researching.

1. Open up a search engine and type in something like "how to convert a pandas column price to integer".

In the first result, I found this Stack Overflow question and answer . Where someone has had the same problem as us and someone else has provided an answer.

Note: Sometimes the answer you're looking for won't be in the first result, or the 2nd or the 3rd. You may have to combine a few different solutions. Or, if possible, you can try and ask ChatGPT to help you out.

2. In practice, you'd read through this and see if it relates to your problem.

3. If it does, you can adjust the code from what's given in the Stack Overflow answer(s) to your own problem.

4. If you're still stuck, you can try and converse with ChatGPT to help you with your problem (as long as the data/problem you're working on is okay to share - never share private data with anyone on the internet, including AI chatbots).

What's important in the beginning is not to remember every single detail off by heart but to know where to look. Remember, if in doubt, write code, run it, see what happens.

Let's copy the answer code here and see how it relates to our problem.

Answer code: dataframe['amount'] = dataframe['amount'].str.replace('[\$\,\.]', '').astype(int)

There's a lot going on here but what we can do is change the parts which aren't in our problem and keep the rest the same.

Our DataFrame is called car_sales not dataframe.

car_sales['amount'] = car_sales['amount'].str.replace('[\$\,\.]', '').astype(int)

And our 'amount' column is called "Price".

car_sales["Price"] = car_sales["Price"].str.replace('[\$\,\.]', '').astype(int)

That looks better. What the code on the right of car_sales["Price"] is saying is "remove the $ sign and comma and change the type of the cell to int".

Let's see what happens.

In [58]:

# Change Price column to integers
car_sales["Price"] = car_sales["Price"].str.replace('[\$\,\.]', '', regex=True)
car_sales

# Change Price column to integers car_sales["Price"] = car_sales["Price"].str.replace('[\$\,\.]', '', regex=True) car_sales

Out[58]:

	Make	Colour	Odometer (KM)	Doors	Price
0	Toyota	White	150043	4	400000
1	Honda	Red	87899	4	500000
2	Toyota	Blue	32549	3	700000
3	BMW	Black	11179	5	2200000
4	Nissan	White	213095	4	350000
5	Toyota	Green	99213	4	450000
6	Honda	Blue	45698	4	750000
7	Honda	Blue	54738	4	700000
8	Toyota	White	60000	4	625000
9	Nissan	White	31600	4	970000

Cool! but there are extra zeros in the Price column.

Let's remove it.

In [59]:

# Remove 2 extra zeros from the price column (2200000 -> 22000) by indexing all but the last two digits
car_sales["Price"] = car_sales["Price"].str[:-2].astype(int)
car_sales

# Remove 2 extra zeros from the price column (2200000 -> 22000) by indexing all but the last two digits car_sales["Price"] = car_sales["Price"].str[:-2].astype(int) car_sales

Out[59]:

	Make	Colour	Odometer (KM)	Doors	Price
0	Toyota	White	150043	4	4000
1	Honda	Red	87899	4	5000
2	Toyota	Blue	32549	3	7000
3	BMW	Black	11179	5	22000
4	Nissan	White	213095	4	3500
5	Toyota	Green	99213	4	4500
6	Honda	Blue	45698	4	7500
7	Honda	Blue	54738	4	7000
8	Toyota	White	60000	4	6250
9	Nissan	White	31600	4	9700

In [60]:

car_sales.dtypes

car_sales.dtypes

Out[60]:

Make             object
Colour           object
Odometer (KM)     int64
Doors             int64
Price             int64
dtype: object

Beautiful! Now let's try to plot it agian.

In [61]:

car_sales["Price"].plot();

car_sales["Price"].plot();

This is one of the many ways you can manipulate data using pandas.

When you see a number of different functions in a row, it's referred to as chaining. This means you add together a series of functions all to do one overall task.

Let's see a few more ways of manipulating data.

6. Manipulating data¶

You've seen an example of one way to manipulate data but pandas has many more.

How many more?

Put it this way, if you can imagine it, chances are, pandas can do it.

Let's start with string methods. Because pandas is based on Python, however you can manipulate strings in Python, you can do the same in pandas.

You can access the string value of a column using .str. Knowing this, how do you think you'd set a column to lowercase?

In [62]:

# Lower the Make column
car_sales["Make"].str.lower()

# Lower the Make column car_sales["Make"].str.lower()

Out[62]:

0    toyota
1     honda
2    toyota
3       bmw
4    nissan
5    toyota
6     honda
7     honda
8    toyota
9    nissan
Name: Make, dtype: object

Notice how it doesn't change the values of the original car_sales DataFrame unless we set it equal to.

In [63]:

# View top 5 rows, Make column not lowered
car_sales.head()

# View top 5 rows, Make column not lowered car_sales.head()

Out[63]:

	Make	Colour	Odometer (KM)	Doors	Price
0	Toyota	White	150043	4	4000
1	Honda	Red	87899	4	5000
2	Toyota	Blue	32549	3	7000
3	BMW	Black	11179	5	22000
4	Nissan	White	213095	4	3500

In [64]:

# Set Make column to be lowered
car_sales["Make"] = car_sales["Make"].str.lower()
car_sales.head()

# Set Make column to be lowered car_sales["Make"] = car_sales["Make"].str.lower() car_sales.head()

Out[64]:

	Make	Colour	Odometer (KM)	Doors	Price
0	toyota	White	150043	4	4000
1	honda	Red	87899	4	5000
2	toyota	Blue	32549	3	7000
3	bmw	Black	11179	5	22000
4	nissan	White	213095	4	3500

Reassigning the column changes it in the original DataFrame. This trend occurs throughout all kinds of data manipulation with pandas.

Some functions have a parameter called inplace which means a DataFrame is updated in place without having to reassign it.

Let's see what it looks like in combination with .fillna(), a function which fills missing data. But the thing is, our table isn't missing any data.

In practice, it's likely you'll work with datasets which aren't complete. What this means is you'll have to decide whether how to fill the missing data or remove the rows which have data missing.

Let's check out what a version of our car_sales DataFrame might look like with missing values.

In [65]:

# Option 1: Import car sales data with missing values from local file (stored on our computer)
car_sales_missing = pd.read_csv("../data/car-sales-missing-data.csv")

# Option 2: Import car sales data with missing values from GitHub (if the file is hosted online)
car_sales_missing = pd.read_csv("https://raw.githubusercontent.com/mrdbourke/zero-to-mastery-ml/master/data/car-sales-missing-data.csv")
car_sales_missing

# Option 1: Import car sales data with missing values from local file (stored on our computer) car_sales_missing = pd.read_csv("../data/car-sales-missing-data.csv") # Option 2: Import car sales data with missing values from GitHub (if the file is hosted online) car_sales_missing = pd.read_csv("https://raw.githubusercontent.com/mrdbourke/zero-to-mastery-ml/master/data/car-sales-missing-data.csv") car_sales_missing

Out[65]:

	Make	Colour	Odometer	Doors	Price
0	Toyota	White	150043.0	4.0	$4,000
1	Honda	Red	87899.0	4.0	$5,000
2	Toyota	Blue	NaN	3.0	$7,000
3	BMW	Black	11179.0	5.0	$22,000
4	Nissan	White	213095.0	4.0	$3,500
5	Toyota	Green	NaN	4.0	$4,500
6	Honda	NaN	NaN	4.0	$7,500
7	Honda	Blue	NaN	4.0	NaN
8	Toyota	White	60000.0	NaN	NaN
9	NaN	White	31600.0	4.0	$9,700

Missing values are shown by NaN in pandas. This can be considered the equivalent of None in Python.

Let's use the .fillna() function to fill the Odometer column with the average of the other values in the same column.

In [66]:

# Fill Odometer column missing values with mean
car_sales_missing["Odometer"].fillna(car_sales_missing["Odometer"].mean(), 
                                     inplace=False) # inplace is set to False by default

# Fill Odometer column missing values with mean car_sales_missing["Odometer"].fillna(car_sales_missing["Odometer"].mean(), inplace=False) # inplace is set to False by default

Out[66]:

0    150043.000000
1     87899.000000
2     92302.666667
3     11179.000000
4    213095.000000
5     92302.666667
6     92302.666667
7     92302.666667
8     60000.000000
9     31600.000000
Name: Odometer, dtype: float64

Now let's check the original car_sales_missing DataFrame.

In [67]:

car_sales_missing

car_sales_missing

Out[67]:

	Make	Colour	Odometer	Doors	Price
0	Toyota	White	150043.0	4.0	$4,000
1	Honda	Red	87899.0	4.0	$5,000
2	Toyota	Blue	NaN	3.0	$7,000
3	BMW	Black	11179.0	5.0	$22,000
4	Nissan	White	213095.0	4.0	$3,500
5	Toyota	Green	NaN	4.0	$4,500
6	Honda	NaN	NaN	4.0	$7,500
7	Honda	Blue	NaN	4.0	NaN
8	Toyota	White	60000.0	NaN	NaN
9	NaN	White	31600.0	4.0	$9,700

Because inplace is set to False (default), there's still missing values in the "Odometer" column.

Instead of using inplace, let's resassign the column to the filled version.

We'll use the syntax df[col] = df[col].fillna(value) to fill the missing values in the "Odometer" column with the average of the other values in the same column.

In [68]:

# Fill the Odometer missing values to the mean with inplace=True
car_sales_missing["Odometer"] = car_sales_missing["Odometer"].fillna(car_sales_missing["Odometer"].mean())

# Fill the Odometer missing values to the mean with inplace=True car_sales_missing["Odometer"] = car_sales_missing["Odometer"].fillna(car_sales_missing["Odometer"].mean())

Now let's check the car_sales_missing DataFrame again.

In [69]:

car_sales_missing

car_sales_missing

Out[69]:

	Make	Colour	Odometer	Doors	Price
0	Toyota	White	150043.000000	4.0	$4,000
1	Honda	Red	87899.000000	4.0	$5,000
2	Toyota	Blue	92302.666667	3.0	$7,000
3	BMW	Black	11179.000000	5.0	$22,000
4	Nissan	White	213095.000000	4.0	$3,500
5	Toyota	Green	92302.666667	4.0	$4,500
6	Honda	NaN	92302.666667	4.0	$7,500
7	Honda	Blue	92302.666667	4.0	NaN
8	Toyota	White	60000.000000	NaN	NaN
9	NaN	White	31600.000000	4.0	$9,700

The missing values in the Odometer column have been filled with the mean value of the same column.

In practice, you might not want to fill a column's missing values with the mean, but this example was to show the difference between inplace=False (default) and inplace=True.

Whichever you choose to use will depend on how you structure your code.

All you have to remember is inplace=False returns a copy of the DataFrame you're working with.

This is helpful if you want to make a duplicate of your current DataFrame and save it to another variable.

Where as, inplace=True makes all the changes directly to the target DataFrame.

We've filled some values but there's still missing values in car_sales_missing. Let's say you wanted to remove any rows which had missing data and only work with rows which had complete coverage.

You can do this using .dropna().

In [70]:

# Remove missing data
car_sales_missing.dropna()

# Remove missing data car_sales_missing.dropna()

Out[70]:

	Make	Colour	Odometer	Doors	Price
0	Toyota	White	150043.000000	4.0	$4,000
1	Honda	Red	87899.000000	4.0	$5,000
2	Toyota	Blue	92302.666667	3.0	$7,000
3	BMW	Black	11179.000000	5.0	$22,000
4	Nissan	White	213095.000000	4.0	$3,500
5	Toyota	Green	92302.666667	4.0	$4,500

It appears the rows with missing values have been removed, now let's check to make sure.

In [71]:

car_sales_missing

car_sales_missing

Out[71]:

	Make	Colour	Odometer	Doors	Price
0	Toyota	White	150043.000000	4.0	$4,000
1	Honda	Red	87899.000000	4.0	$5,000
2	Toyota	Blue	92302.666667	3.0	$7,000
3	BMW	Black	11179.000000	5.0	$22,000
4	Nissan	White	213095.000000	4.0	$3,500
5	Toyota	Green	92302.666667	4.0	$4,500
6	Honda	NaN	92302.666667	4.0	$7,500
7	Honda	Blue	92302.666667	4.0	NaN
8	Toyota	White	60000.000000	NaN	NaN
9	NaN	White	31600.000000	4.0	$9,700

Hmm, they're still there, can you guess why?

It's because .dropna() has inplace=False as default. We can either set inplace=True or reassign the car_sales_missing DataFrame.

In [72]:

# The following two lines do the same thing
car_sales_missing.dropna(inplace=True) # Operation happens inplace without reassignment
car_sales_missing = car_sales_missing.dropna() # car_sales_missing gets reassigned to same DataFrame but with dropped values

# The following two lines do the same thing car_sales_missing.dropna(inplace=True) # Operation happens inplace without reassignment car_sales_missing = car_sales_missing.dropna() # car_sales_missing gets reassigned to same DataFrame but with dropped values

Now if check again, the rows with missing values are gone and the index numbers have been updated.

In [73]:

car_sales_missing

car_sales_missing

Out[73]:

	Make	Colour	Odometer	Doors	Price
0	Toyota	White	150043.000000	4.0	$4,000
1	Honda	Red	87899.000000	4.0	$5,000
2	Toyota	Blue	92302.666667	3.0	$7,000
3	BMW	Black	11179.000000	5.0	$22,000
4	Nissan	White	213095.000000	4.0	$3,500
5	Toyota	Green	92302.666667	4.0	$4,500

Instead of removing or filling data, what if you wanted to create it?

For example, creating a column called Seats for number of seats.

pandas allows for simple extra column creation on DataFrame's.

Three common ways are:

1. Adding a pandas.Series as a column.
2. Adding a Python list as a column.
3. By using existing columns to create a new column.

In [74]:

# Create a column from a pandas Series
seats_column = pd.Series([5, 5, 5, 5, 5, 5, 5, 5, 5, 5])
car_sales["Seats"] = seats_column
car_sales

# Create a column from a pandas Series seats_column = pd.Series([5, 5, 5, 5, 5, 5, 5, 5, 5, 5]) car_sales["Seats"] = seats_column car_sales

Out[74]:

	Make	Colour	Odometer (KM)	Doors	Price	Seats
0	toyota	White	150043	4	4000	5
1	honda	Red	87899	4	5000	5
2	toyota	Blue	32549	3	7000	5
3	bmw	Black	11179	5	22000	5
4	nissan	White	213095	4	3500	5
5	toyota	Green	99213	4	4500	5
6	honda	Blue	45698	4	7500	5
7	honda	Blue	54738	4	7000	5
8	toyota	White	60000	4	6250	5
9	nissan	White	31600	4	9700	5

Creating a column is similar to selecting a column, you pass the target DataFrame along with a new column name in brackets.

In [75]:

# Create a column from a Python list
engine_sizes = [1.3, 2.0, 3.0, 4.2, 1.6, 1, 2.0, 2.3, 2.0, 3.0]
car_sales["Engine Size"] = engine_sizes
car_sales

# Create a column from a Python list engine_sizes = [1.3, 2.0, 3.0, 4.2, 1.6, 1, 2.0, 2.3, 2.0, 3.0] car_sales["Engine Size"] = engine_sizes car_sales

Out[75]:

	Make	Colour	Odometer (KM)	Doors	Price	Seats	Engine Size
0	toyota	White	150043	4	4000	5	1.3
1	honda	Red	87899	4	5000	5	2.0
2	toyota	Blue	32549	3	7000	5	3.0
3	bmw	Black	11179	5	22000	5	4.2
4	nissan	White	213095	4	3500	5	1.6
5	toyota	Green	99213	4	4500	5	1.0
6	honda	Blue	45698	4	7500	5	2.0
7	honda	Blue	54738	4	7000	5	2.3
8	toyota	White	60000	4	6250	5	2.0
9	nissan	White	31600	4	9700	5	3.0

You can also make a column by directly combining the values of other columns. Such as, price per kilometre on the Odometer.

In [76]:

# Column from other columns
car_sales["Price per KM"] = car_sales["Price"] / car_sales["Odometer (KM)"]
car_sales

# Column from other columns car_sales["Price per KM"] = car_sales["Price"] / car_sales["Odometer (KM)"] car_sales

Out[76]:

	Make	Colour	Odometer (KM)	Doors	Price	Seats	Engine Size	Price per KM
0	toyota	White	150043	4	4000	5	1.3	0.026659
1	honda	Red	87899	4	5000	5	2.0	0.056883
2	toyota	Blue	32549	3	7000	5	3.0	0.215060
3	bmw	Black	11179	5	22000	5	4.2	1.967976
4	nissan	White	213095	4	3500	5	1.6	0.016425
5	toyota	Green	99213	4	4500	5	1.0	0.045357
6	honda	Blue	45698	4	7500	5	2.0	0.164121
7	honda	Blue	54738	4	7000	5	2.3	0.127882
8	toyota	White	60000	4	6250	5	2.0	0.104167
9	nissan	White	31600	4	9700	5	3.0	0.306962

Now can you think why this might not be a great column to add?

It could be confusing when a car with less kilometers on the odometer looks to cost more per kilometre than one with more.

When buying a car, usually less kilometres on the odometer is better.

This kind of column creation is called feature engineering, the practice of enriching your dataset with more information (either from it directly or elsewhere).

If Make, Colour, Doors are features of the data, creating Price per KM could be another. But in this case, not a very good one.

As for column creation, you can also create a new column setting all values to a one standard value.

In [77]:

# Column to all 1 value (number of wheels)
car_sales["Number of wheels"] = 4
car_sales

# Column to all 1 value (number of wheels) car_sales["Number of wheels"] = 4 car_sales

Out[77]:

	Make	Colour	Odometer (KM)	Doors	Price	Seats	Engine Size	Price per KM	Number of wheels
0	toyota	White	150043	4	4000	5	1.3	0.026659	4
1	honda	Red	87899	4	5000	5	2.0	0.056883	4
2	toyota	Blue	32549	3	7000	5	3.0	0.215060	4
3	bmw	Black	11179	5	22000	5	4.2	1.967976	4
4	nissan	White	213095	4	3500	5	1.6	0.016425	4
5	toyota	Green	99213	4	4500	5	1.0	0.045357	4
6	honda	Blue	45698	4	7500	5	2.0	0.164121	4
7	honda	Blue	54738	4	7000	5	2.3	0.127882	4
8	toyota	White	60000	4	6250	5	2.0	0.104167	4
9	nissan	White	31600	4	9700	5	3.0	0.306962	4

In [78]:

car_sales["Passed road safety"] = True
car_sales

car_sales["Passed road safety"] = True car_sales

Out[78]:

	Make	Colour	Odometer (KM)	Doors	Price	Seats	Engine Size	Price per KM	Number of wheels	Passed road safety
0	toyota	White	150043	4	4000	5	1.3	0.026659	4	True
1	honda	Red	87899	4	5000	5	2.0	0.056883	4	True
2	toyota	Blue	32549	3	7000	5	3.0	0.215060	4	True
3	bmw	Black	11179	5	22000	5	4.2	1.967976	4	True
4	nissan	White	213095	4	3500	5	1.6	0.016425	4	True
5	toyota	Green	99213	4	4500	5	1.0	0.045357	4	True
6	honda	Blue	45698	4	7500	5	2.0	0.164121	4	True
7	honda	Blue	54738	4	7000	5	2.3	0.127882	4	True
8	toyota	White	60000	4	6250	5	2.0	0.104167	4	True
9	nissan	White	31600	4	9700	5	3.0	0.306962	4	True

Now you've created some columns, you decide to show your colleague what you've done. When they ask about the Price per KM column, you tell them you're not really sure why it's there.

You decide you better remove it to prevent confusion.

You can remove a column using .drop('COLUMN_NAME', axis=1).

In [79]:

# Drop the Price per KM column
car_sales = car_sales.drop("Price per KM", axis=1) # columns live on axis 1
car_sales

# Drop the Price per KM column car_sales = car_sales.drop("Price per KM", axis=1) # columns live on axis 1 car_sales

Out[79]:

	Make	Colour	Odometer (KM)	Doors	Price	Seats	Engine Size	Number of wheels	Passed road safety
0	toyota	White	150043	4	4000	5	1.3	4	True
1	honda	Red	87899	4	5000	5	2.0	4	True
2	toyota	Blue	32549	3	7000	5	3.0	4	True
3	bmw	Black	11179	5	22000	5	4.2	4	True
4	nissan	White	213095	4	3500	5	1.6	4	True
5	toyota	Green	99213	4	4500	5	1.0	4	True
6	honda	Blue	45698	4	7500	5	2.0	4	True
7	honda	Blue	54738	4	7000	5	2.3	4	True
8	toyota	White	60000	4	6250	5	2.0	4	True
9	nissan	White	31600	4	9700	5	3.0	4	True

Why axis=1? Because that's the axis columns live on. Rows live on axis=0.

Let's say you wanted to shuffle the order of your DataFrame so you could split it into train, validation and test sets. And even though the order of your samples was random, you wanted to make sure.

To do so you could use .sample(frac=1).

.sample() randomly samples different rows from a DataFrame.

The frac parameter dictates the fraction, where 1 = 100% of rows, 0.5 = 50% of rows, 0.01 = 1% of rows.

You can also use .sample(n=1) where n is the number of rows to sample.

In [80]:

# Sample car_sales
car_sales_sampled = car_sales.sample(frac=1)
car_sales_sampled

# Sample car_sales car_sales_sampled = car_sales.sample(frac=1) car_sales_sampled

Out[80]:

	Make	Colour	Odometer (KM)	Doors	Price	Seats	Engine Size	Number of wheels	Passed road safety
2	toyota	Blue	32549	3	7000	5	3.0	4	True
4	nissan	White	213095	4	3500	5	1.6	4	True
9	nissan	White	31600	4	9700	5	3.0	4	True
1	honda	Red	87899	4	5000	5	2.0	4	True
0	toyota	White	150043	4	4000	5	1.3	4	True
8	toyota	White	60000	4	6250	5	2.0	4	True
3	bmw	Black	11179	5	22000	5	4.2	4	True
5	toyota	Green	99213	4	4500	5	1.0	4	True
6	honda	Blue	45698	4	7500	5	2.0	4	True
7	honda	Blue	54738	4	7000	5	2.3	4	True

Notice how the rows remain intact but their order is mixed (check the indexes).

.sample(frac=X) is also helpful when you're working with a large DataFrame.

Say you had 2,000,000 rows.

Running tests, analysis and machine learning algorithms on 2,000,000 rows could take a long time. And since being a data scientist or machine learning engineer is about reducing the time between experiments, you might begin with a sample of rows first.

For example, you could use 40k_rows = 2_mil_rows.sample(frac=0.05) to work on 40,000 rows from a DataFrame called 2_mil_rows containing 2,000,000 rows.

What if you wanted to get the indexes back in order?

You could do so using .reset_index().

In [81]:

# Reset the indexes of car_sales_sampled
car_sales_sampled.reset_index()

# Reset the indexes of car_sales_sampled car_sales_sampled.reset_index()

Out[81]:

	index	Make	Colour	Odometer (KM)	Doors	Price	Seats	Engine Size	Number of wheels	Passed road safety
0	2	toyota	Blue	32549	3	7000	5	3.0	4	True
1	4	nissan	White	213095	4	3500	5	1.6	4	True
2	9	nissan	White	31600	4	9700	5	3.0	4	True
3	1	honda	Red	87899	4	5000	5	2.0	4	True
4	0	toyota	White	150043	4	4000	5	1.3	4	True
5	8	toyota	White	60000	4	6250	5	2.0	4	True
6	3	bmw	Black	11179	5	22000	5	4.2	4	True
7	5	toyota	Green	99213	4	4500	5	1.0	4	True
8	6	honda	Blue	45698	4	7500	5	2.0	4	True
9	7	honda	Blue	54738	4	7000	5	2.3	4	True

Calling .reset_index() on a DataFrame resets the index numbers to their defaults. It also creates a new Index column by default which contains the previous index values.

Finally, what if you wanted to apply a function to a column. Such as, converting the Odometer column from kilometers to miles.

You can do so using the .apply() function and passing it a Python lambda function. We know there's about 1.6 kilometers in a mile, so if you divide the value in the Odometer column by 1.6, it should convert it to miles.

In [82]:

# Change the Odometer values from kilometres to miles
car_sales["Odometer (KM)"].apply(lambda x: x / 1.6)

# Change the Odometer values from kilometres to miles car_sales["Odometer (KM)"].apply(lambda x: x / 1.6)

Out[82]:

0     93776.875
1     54936.875
2     20343.125
3      6986.875
4    133184.375
5     62008.125
6     28561.250
7     34211.250
8     37500.000
9     19750.000
Name: Odometer (KM), dtype: float64

Now let's check our car_sales DataFrame.

In [83]:

car_sales

car_sales

Out[83]:

	Make	Colour	Odometer (KM)	Doors	Price	Seats	Engine Size	Number of wheels	Passed road safety
0	toyota	White	150043	4	4000	5	1.3	4	True
1	honda	Red	87899	4	5000	5	2.0	4	True
2	toyota	Blue	32549	3	7000	5	3.0	4	True
3	bmw	Black	11179	5	22000	5	4.2	4	True
4	nissan	White	213095	4	3500	5	1.6	4	True
5	toyota	Green	99213	4	4500	5	1.0	4	True
6	honda	Blue	45698	4	7500	5	2.0	4	True
7	honda	Blue	54738	4	7000	5	2.3	4	True
8	toyota	White	60000	4	6250	5	2.0	4	True
9	nissan	White	31600	4	9700	5	3.0	4	True

The Odometer column didn't change. Can you guess why?

We didn't reassign it.

In [84]:

# Reassign the Odometer column to be miles instead of kilometers
car_sales["Odometer (KM)"] = car_sales["Odometer (KM)"].apply(lambda x: x / 1.6)
car_sales

# Reassign the Odometer column to be miles instead of kilometers car_sales["Odometer (KM)"] = car_sales["Odometer (KM)"].apply(lambda x: x / 1.6) car_sales

Out[84]:

	Make	Colour	Odometer (KM)	Doors	Price	Seats	Engine Size	Number of wheels	Passed road safety
0	toyota	White	93776.875	4	4000	5	1.3	4	True
1	honda	Red	54936.875	4	5000	5	2.0	4	True
2	toyota	Blue	20343.125	3	7000	5	3.0	4	True
3	bmw	Black	6986.875	5	22000	5	4.2	4	True
4	nissan	White	133184.375	4	3500	5	1.6	4	True
5	toyota	Green	62008.125	4	4500	5	1.0	4	True
6	honda	Blue	28561.250	4	7500	5	2.0	4	True
7	honda	Blue	34211.250	4	7000	5	2.3	4	True
8	toyota	White	37500.000	4	6250	5	2.0	4	True
9	nissan	White	19750.000	4	9700	5	3.0	4	True

If you've never seen a lambda function they can be tricky. What the line above is saying is "take the value in the Odometer (KM) column (x) and set it to be itself divided by 1.6".

Summary¶

Main topics we covered¶

• Series - a single column (can be multiple rows) of values.
• DataFrame - multiple columns/rows of values (a DataFrame is comprised of multiple Series).
• Importing data - we used pd.read_csv() to read in a CSV (comma-separated values) file but there are multiple options for reading data.
• Exporting data - we exported our data using to_csv(), however there are multiple methods of exporting data.
• Describing data
  • df.dtypes - find the datatypes present in a dataframe.
  • df.describe() - find various numerical features of a dataframe.
  • df.info() - find the number of rows and whether or not any of them are empty.
• Viewing and selecting data
  • df.head() - view the first 5 rows of df.
  • df.loc & df.iloc - select specific parts of a dataframe.
  • df['A'] - select column A of df.
  • df[df['A'] > 1000] - selection column A rows with values over 1000 of df.
  • df['A'] - plot values from column A using matplotlib (defaults to line graph).
• Manipulating data and performing operations - pandas has many built-in functions you can use to manipulate data, also many of the Python operators (e.g. +, -, >, ==) work with pandas.

Further reading¶

Since pandas is such a large library, it would be impossible to cover it all in one go.

The following are some resources you might want to look into for more.

• Python for Data Analysis by Wes McKinney - possibly the most complete text of the pandas library (apart from the documentation itself) written by the creator of pandas.
• Data Manipulation with Pandas (section of Python Data Science Handbook by Jake VanderPlas) - a very hands-on approach to many of the main functions in the pandas library.

Exercises¶

After completing this notebook, you next thing should be to try out some more pandas code of your own.

I'd suggest at least going through number 1 (write out all the code yourself), a couple from number 2 (again, write out the code yourself) and spend an hour reading number 3 (this is vast but keep it in mind).

1. 10-minute introduction to pandas - go through all the functions here and be sure to write out the code yourself.
2. Pandas getting started tutorial - pick a couple from here which spark your interest and go through them both writing out the code for your self.
3. Pandas essential basic functionality - spend an hour reading this and bookmark it for whenever you need to come back for an overview of pandas.