Pandas data analysis tool for Python

Pandas : Python Data analysis tool.

DataFrame	Pandas DataFrame
iloc	Values at different position using integer
loc	Values at different position using column label
filter	Condition based filtering of rows
methods	Pandas DataFrame methods
read_sql	Using MySql records to create Pandas DataFrame
read_csv	Reading data from csv file
read_excel	Reading data from Excel file
read_json	Reading data from Json file
sort_values	Sort columns in ascending or descending
to_sql	Using DataFrame managing MySql database
to_csv	Saving data to CSV file
to_json	Saving / output data in Json format
to_excel	Saving data to Excel file
groupby	combining data and aggregate functions
merge	combining data and aggregate functions
count	Number of rows or columns with different options
sum	Sum of values of requried axis
max	Max value of requried axis
min	Min value of requried axis
astype	Cast to a specified dtype
Exercise	Using groupby and merge of DataFrame
Exercise2	Using str.contains(), max(), min(),len() of DataFrame

Filtering records

loc	Values at different position using column label
rows	Filtering rows based on data

handling string using str methods

str.contains	string matching against data columns
str.contains.sum	Max Min Sum of any column
Convert Case	Lower to Upper and vice versa
split()	Breaking string using delimiter
slice()	Substring by breaking string
cat()	Concatenate strings
count()	Number of occurences of pattern
replace()	Replace part of string by regex
len()	Length of the data in our DataFrame

Plotting Graphs using Data

Plotting graphs	Creating different type of graphs using DataFrame

Excel to MySQL

import pandas as pd 
my_data = pd.read_excel('D:\emp.xlsx')
# reading data from root of D drive. 
from sqlalchemy import create_engine
engine = create_engine("mysql+mysqldb://userid:password@localhost/my_tutorial")

### Creating new table emp or appending existing table 
my_data.to_sql(con=engine,name='emp',if_exists='append')

MySQL to Excel

import pandas as pd 

from sqlalchemy import create_engine
engine = create_engine("mysql+mysqldb://userid:password@localhost/my_tutorial")

sql="SELECT * FROM emp "
my_data = pd.read_sql(sql,engine )

my_data.to_excel('D:\emp2.xlsx')

Pandas DataFrame

Numpy arrys Python & MySQL Python- Tutorials