Pandas is a great python library for data handling. It declares 2 main classes:

1. Series – for one dimensional array
2. DataFrame – for 2 dimensional tables (and with multi-indexing can display more dimensions)

Typical flow of using Pandas will be – load the data, manipulate and store again. This is very similar to SQL use with Select, Insert, Update and Delete statement

In this post i will cover the basic operations in pandas compared to SQL statements

I will use a simple CSV file, load it to a  dataframe and run all the commands on it:

import numpy as np 
import pandas as pd 

df=pd.read_csv('pupils.csv')
df.head()

Select

Select * from df where index between 2 and 6

df[2:6]

Select * from df where (index between 3 and 9) and (index % 2 = 1)

df[3:9:2]

Select Name from df

df['Name']

Select Name, Age from df

df[['Name','Age']]

Select Name where index between 2 and 6

df['Name'][2:6]

Select Name,Age where index between 2 and 6

df[['Name','Age']][2:6]

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Note for reference – Same operations with loc:

df.loc[2] # index 2 as series
df.loc[2:2] # index 2 as dataframe
df.loc[2:6] # indexes 2-6
df.loc[2:6:2] # even indexes 2-6 
df.loc[2:2]['Age'] # Age where index=2
df.loc[1,'Age'] # Age where index=1
df.loc[1:13:2,'Age':'income':2] # odd rows from 1 to 13 and even cols from Age to income

Same operations with iloc:

df.iloc[1] # where index=1
df.iloc[1,1] # Age where index=1
df.iloc[1:4] # where index between 1 and 3
df.iloc[1:4,2:4] # Country, Height where index between 1 and 3

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Select * from df where Age > 12

df[df['Age']>10]

Select Name, Country  from df where Age > 12

df[df['Age']>10][['Name','Country']]

Select * from df where Age>12 or Height > 130

df[(df['Age']>12) | (df['Height'] > 130)]

Select * from df where Age>12 and Height > 130

df[(df['Age']>12) & (df['Height'] > 130)]

 

Insert

Insert into df values (‘eli’,4,’DF’,100,20,20,2000,4,4)

df.loc[df.index.size]=['eli',4,'DF',100,20,20,2000,4,4]

Insert into df select * from df2

df.append(df2)

Insert new column with values (alter table df add Inc int; update df set Inc=income*2)

df['Inc']=df['income']*2

 

Update

Update df set Age=30

df.loc[:,'Age']=30

Update df set Age=20 where income>20000

df.loc[df['income']>20000,'Age']=20

Update df set income=income*2

df['income']*=2

Complex Update using iterator:

for i,v in df.iterrows():
    if v.Age > 10:
        df.loc[i,'Weight'] = 888

The above is a simple example but you can add as much logic as you need to the loop

 

Delete

Delete df where index=6

df.drop(6)

Delete df where Age=6

df.drop(df['Age']==6,inplace=True)

Delete column (alter table drop column Age)

df.drop('Age',axis=1)

 

Aggregates and Group By

Select count(*) from df

df.agg('count')

Select count(*),max(*) from df

df.agg(['count','max'])

Select count(Name), sum(income) , max(Age) from df

df.agg({'Name':'count', 'income':'sum' , 'Age':'max'})

select Country,count(*) from df group by Country

df.groupby('Country').count()

select Country,[family persons],count(*) from df group by Country,[family persons]

df1=df.groupby(['Country','family persons']).count()