to_pickle() to pickle pandas DataFrame and save using MySQL Excel and CSV sources

Pandas Pandas input output

`path ( file )`	Path with file name to store the byte string of pickled data
`compression`	(Optional )Type of compression, {'infer', 'gzip', 'bz2', 'zip', 'xz', None}
`protocol`	(Optional) , Int, we can use HIGHEST_PROTOCOL to get the latest

We can create a file with DataFrame data by using to_pickle(). We will create one DataFrame by using a dictionary.
What is pickle ?

Python Pandas DataFrame output as pickle & using Excel or MySQL table as source using to_pickle()

import pandas as pd
import pickle
my_dict={
	'NAME':['Ravi','Raju','Alex'],
	'ID':[1,2,3],'MATH':[30,40,50],
	'ENGLISH':[20,30,40]
	}
df = pd.DataFrame(data=my_dict)
df.to_pickle("my_data.pkl") # pickled

This is the data written to the current directory. Check for the file my_data.pkl . You can use path to store the file in different location.

df.to_pickle("D:\my_data\my_data.pkl")

Compression

We can add compression option. ( You must take care while reading the same file )

df.to_pickle("my_data.pkl",compression='zip')

protocol

We can assign the integer or we can get the latest by using HIGHEST_PROTOCOL

df.to_pickle("my_data.pkl",protocol=pickle.HIGHEST_PROTOCOL)

Using SQLAlchemy

We will collect records from our sample student table in MySQL database and create the pickle file by using to_pickle().
Collect SQL dump of sample student table below.
Read more on MySQL with SQLAlchemy connection. you can add path if you want the file to be created using to_pickle() ( sample is given above )

import pandas as pd 
from sqlalchemy import create_engine
my_conn = create_engine("mysql+mysqldb://userid:pw@localhost/my_db")
sql="SELECT * FROM student LIMIT 0,10 "
df = pd.read_sql(sql,my_conn)
df.to_pickle("D:\my_data\my_data.pkl")

From Excel file to Pickle output

In above code we have created one DataFrame by taking data from a MySQL database table. We can create DataFrame by using any excel data or by using any csv file or from any other sources. ( check here to create a DataFrame from 8 different sources )
Once a DataFrame is created, then using that we can create pickle output by using to_pickle(). Here is one example to read one Excel file to a DataFrame and generate the string, you can explore other sources to create a DataFrame and finally generate pickle / file.
We used read_excel() to read our sample student.xlsx file.

df=pd.read_excel("D:\\my_data\\student.xlsx") # Path of the file. 
df.to_pickle("D:\my_data\my_data.pkl")

We can read one csv file by using read_csv()

df=pd.read_csv("D:\\my_data\\student.csv") # change the path
df.to_pickle("D:\my_data\my_data.pkl")

While creating the pickle we have to note the compression used. When we de-serialize or un-pickle the same file, we must use the same type of compression.

read_pickle()
Read how to pickle data stored in a MySQL table.
Pandas read_clipboard() read_html() read_csv() read_excel() to_excel()
Data input and output from Pandas DataFrame

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