Posts Tagged ‘sql’

Pandas for SQL lovers – JOIN statements

This is panda5the next post in the Pandas for SQL lovers series. You can find earlier posts here:

When you have data stored in two database tables you can use a JOIN statement to write a query to join the data stored across the two tables.

The Pandas merge function provides similar functionality for DataFrames.

Start with a csv file loaded into a DataFrame containing flight information which tells you the airport code for the airport where the flight originates.

import pandas as pd
flights_df = pd.read_csv('flight_data_part1.csv')

Flight information including airport codes such ABQ or DUL

The mapping of airport codes to airport names is stored in a separate file which you load into it’s own DataFrame.

airport_codes_df = pd.read_csv('AirportCodeList.csv')

List of airport codes and names

You might need too merge the two DataFrames the same way you might want to do a JOIN statement across two databases tables so you can analyze data by city as well as airport code.

To accomplish the same functionality with Pandas use the merge function.

When you call merge you must specify two DataFrames and the columns to use to identify matching rows.

  • The left_on parameter specifes the name of the column in the first DataFrame listed, which appears on the left when you read the command from left to right.
  • The right_on parameter specifes the name of the column in the second DataFrame listed, which appears on the right when you read the command from left to right.

merged_df = pd.merge(flights_df,airport_codes_df,left_on='ORIGIN',right_on='CODE')
print (merged_df[['FL_DATE','OP_CARRIER_FL_NUM','ORIGIN','CODE','CITY']])

Data showing flight information and airport codes and names in a single data frame

By default merge performs an inner join. This means a record is only returned if a match is found in both DataFrames. i.e. if you have an airport code, but no flights for that airport, you will not see a row for that airport code in the merged DataFrame. Equally true, if there is a flight with an airport code that is not listed in the airport codes list, that flight information will not appear in the merged DataFrame.

If you want to display all the records from one of the DataFrames regardless of whether there is a match  in the other DataFrame you must perform an outer join by specifying a value for the how parameter.

  • right – to display all records from the right dataframe regardless of whether a match is found.
  • left – to display all records from the left DataFrame regardless of whether a match is found.
  • outer – to display all recrods from btoh DtaFrames regardless of whether a match is found.

When there is no matching records NaN is used for the missing values.

merged_df = pd.merge(flights_df,extra_airport_codes_df,left_on='ORIGIN',right_on='CODE',how='right')
print (merged_df[['FL_DATE','OP_CARRIER_FL_NUM','ORIGIN','CODE','CITY']])

DataFrame with extra row for airport code with no matching flights

Happy coding!

Pandas for SQL Lovers Part 2: INSERT / Populating a DataFrame

SQLPanda2Know SQL and trying to learn Python Pandas for data science? Many concepts are the same. In this series I explain Pandas using SQL as a reference point.

In this post I will explain how to populate a Pandas DataFrame. If you are not familiar with how to create a Pandas DataFrame check out Part 1 Creating DataFrames

INSERT INTO

In SQL if you want to insert rows into a table you use the INSERT statement. You can insert a single row


INSERT INTO players
(FirstName, LastName, Team, Position, JerseyNumber, Salary, Birthdate)
VALUES
('Joe','Pavelski','SJ','C',8,6000000.00,'1984-07-11')

OR you can insert multiple rows with a single INSERT statement


INSERT INTO players
(FirstName, LastName, Team, Position, JerseyNumber, Salary, Birthdate)
VALUES
('Joe','Pavelski','SJ','C',8,6000000.00,'1984-07-11'),
('Connor','McDavid','EDM','C','97,925000.00,'1997-01-13')

Populating a DataFrame when created

When you create your DataFrame, you can provide data and populate it immediately.


column_names = ['FirstName',
'LastName',
'Team',
'Position',
'JerseyNumber',
'Salary',
'Birthdate']
data = [['Joe','Pavelski','SJ','C',8,6000000.00,'1984-07-11'],
['Connor','McDavid','EDM','C',97,925000.00,'1997-01-13'],
['Sidney','Crosby','PIT','C',87,8700000.00,'1987-08-07'],
['Carey','Price','MTL','G',31,10500000.00,'1987-08-16']]
players  = pd.DataFrame(data, columns=column_names)

This creates the following DataFrame

PlayersDataFrame

Inferred Datatypes

The advantage to populating the DataFrame when it is created, is that Pandas will infer the datatypes based on the data. If I run the command:


players.dtypes

I can see the DataFrame assigned integer and float datatypes to JerseyNumber and Salary. All the other columns are strings (because strings are stored as a sequence the datatype displayed is object):

FirstName object
LastName object
Team object
Position object
JerseyNumber int64
Salary float64
Birthdate object

Explicit Datatypes

If you want BirthDate to be a date, datatype you will need to convert it explicitly. The line of code below uses to_datetime to convert the Birthdate column to a datetime:

players['Birthdate']= pd.to_datetime(players['Birthdate'])

Now the Birthdate column stores the datatype datetime:

FirstName object
LastName object
Team object
Position object
JerseyNumber int64
Salary float64
Birthdate datetime64[ns]

You can find more details on how to assign datatypes explicitly in the Part 1 of this series: how to create DataFrames.

Indexes in DataFrames

You may have noticed that I did not have any sort of ‘playerid’ value for the rows I inserted. But you can see a number beside each row.  This column is called the index. Pandas will automatically create an index for each row in the DataFrame.

PlayersDataFrame

Setting your own column as index

If you want to use your own column for the index, you can use set_index. The example below creates a DataFrame with a PlayerId and then users set_index to make PlayerId the index column.

column_names = ['PlayerId',
'FirstName',
'LastName',
'Team',
'Position',
'JerseyNumber',
'Salary',
'Birthdate']
data = [[1,'Joe','Pavelski','SJ','C',8,6000000.00,'1984-07-11'],
[2,'Connor','McDavid','EDM','C',97,925000.00,'1997-01-13'],
[3, 'Sidney','Crosby','PIT','C',87,8700000.00,'1987-08-07'],
[4, 'Carey','Price','MTL','G',31,10500000.00,'1987-08-16']]
players  = pd.DataFrame(data,columns=column_names)

players.set_index(‘PlayerId’, inplace=True)

This produces a DataFrame with PlayerId as the Index column

PlayerIdIndex

Using non numeric columns as an index

You are not limited to numeric fields as indexes, you can use any field as your index:

 players.set_index('LastName', inplace=True)

LastNameIndex

Duplicate values in index columns

Unlike an Primary Key in a database, the index on a DataFrame will allow duplicate values. If you decide to use LastName as your index column and you have the Henrik & Daniel Sedin in your DataFrame you will see duplicate indexes.

duplicateIndex

Adding rows to an existing DataFrame

If you want to add rows to a DataFrame after it is created use append. In the code below let’s recreate the populated DataFrame with the autogenerated index:

column_names = ['FirstName',
'LastName',
'Team',
'Position',
'JerseyNumber',
'Salary',
'Birthdate']
data = [['Joe','Pavelski','SJ','C',8,6000000.00,'1984-07-11'],
['Connor','McDavid','EDM','C',97,925000.00,'1997-01-13'],
['Sidney','Crosby','PIT','C',87,8700000.00,'1987-08-07'],
['Carey','Price','MTL','G',31,10500000.00,'1987-08-16']]
players = pd.DataFrame(data, columns=column_names)

PlayersDataFrame

Now you can use append to add a row. A couple of things to be aware of:

  • Using append creates a new DataFrame with the added row, if you want to append the row to your existing DataFrame you need to assign the result of the append to your original DataFrame.
  • You must specify ignore_index = True when you are providing explicit values to use for the new row being appended

players = players.append({'FirstName':'Erik',
'LastName':'Karlsson',
'Team':'SJ',
'Position':'D',
'JerseyNumber':65,
'Salary':11500000.00,
'Birthdate':'1990-05-31'},
ignore_index=True)

AppendedOneRow

Summary

Now you can add rows to your DataFrame. In upcoming posts we will look at how to populate your DataFrame from a CSV file or from a database table.

Python Pandas for SQL fans Part 1: Creating DataFrames

panda holding SQL heartI have worked with data for years, and I am very familiar with SQL. When I started exploring data science I found a number of my SQL skills translated well into data science. Pandas tends to be the most popular python library for manipulating data for those familiar with SQL, so I figured I would write a series of posts on how to use Pandas written from a SQL perspective!

In this post we will focus on how to create a DataFrame this is the equivalent of creating a table in a database.

Part 2 shows you How to insert data into a DataFrame.

Pre-requisites

You need to import the python pandas and numpy libraries to use any of the code examples below:

import pandas as pd
import numpy as np

CREATE TABLE

If you want to query data in a database, you need to create a table. If you want to query data in Pandas, you need to create a DataFrame.

If I want to create a database table to hold information about hockey players I would use the CREATE TABLE statement:

CREATE TABLE players (
first_name   VARCHAR(30),
last_name VARCHAR(30),
team VARCHAR(3),
position VARCHAR(2),
jersey_number INT,
salary DECIMAL,
birthdate DATETIME)

Create Pandas DataFrame

To create a DataFrame to hold the same information in pandas, all I need to do is define the column names and create a DataFrame using the column name:

column_names = ['FirstName',
'LastName',
'Team',
'Position',
'JerseyNumber',
'Salary',
'Birthdate']
players  = pd.DataFrame(columns=column_names)

Creates an empty dataframe:

Empty DataFrame
Columns: [FirstName, LastName, Team, Position, JerseyNumber, Salary, Birthdate]

Specifying data types

Now you probably noticed I didn’t specify the datatypes for my columns when I created the dataframe. You can display the datatypes for your DataFrame using dtypes:

players.dtypes

which returns:

FirstName object
LastName object
Team object
Position object
JerseyNumber object
Salary object
Birthdate object

Any column with a datatype of string will be listed as object because in Python a string is actually a sequence of characters which does allow you some neat slicing to retrieve substrings.

You can modify the datatypes of the columns after the DataFrame is created using astype:

players = players.astype({'FirstName':str,
'LastName':str,
'Team':str,
'Position':str,
'JerseyNumber':int,
'Salary':float,
'Birthdate':np.datetime64})

Now I have the datatypes:

FirstName object
LastName object
Team object
Position object
JerseyNumber int32
Salary float64
Birthdate datetime64[ns]

Wondering what datatypes are available? Chris Moffitt wrote a nice post summarizing Pandas data types. You can also refer to the official Pandas documentation on dtypes.

If you are wondering, why you can’t specify datatypes for each column when a DataFrame is created, that’s because unlike when you work with database tables, you usually create DataFrames from a dataset and the datatype is inferred from the data. The DataFrame constructor does accept a datatype argument, but you can only use it to specify a datatype to use for all columns in the DataFrame, you cannot specify different datatypes for each column.

Converting all columns to a single datatype

If all your columns will be the same dataype, you can use astype to convert all columns to a new datatype:

column_names = ['FirstName',
'LastName',
'Team',
'Position',
'JerseyNumber',
'Salary',
'Birthdate']
players  = pd.DataFrame(columns=column_names)
players = players.astype(int)

would give me a DataFrame with every column as an integer:

FirstName int32
LastName int32
Team int32
Position int32
JerseyNumber int32
Salary int32
Birthdate int32

Converting one column to a new datatype

You can also convert a single column in a DataFrame to a new datatype using to_datetime, to_numeric, to timedelta

NOTE: Timedelta is a datatype in python used to specify a gap between two dates and times.

The following code shows how I could use these functions to convert the columns that are not storing strings:

column_names = ['FirstName',
'LastName',
'Team',
'Position',
'JerseyNumber',
'Salary',
'Birthdate']
players  = pd.DataFrame(columns=column_names)
players['Birthdate']= pd.to_datetime(players['Birthdate'])
players['JerseyNumber']=pd.to_numeric(players['JerseyNumber'],downcast='integer')
players['Salary']=pd.to_numeric(players['Salary'],downcast='float')

Would give me a DataFrame with the data types:

FirstName object
LastName object
Team object
Position object
JerseyNumber int8
Salary float32
Birthdate datetime64[ns]

Note that to_numeric requires an additional parameter downcast to specify the type of numeric datatype required.

Coming next

In the Part 2 you learn the equivalent of the INSERT statement, and learn how to add data to your DataFrame.