Create an excel file using pandas in python:

users = [{
     'user_id': 1
 }, {
     'user_id': 2
 }]
 df = pandas.DataFrame(users, columns=['user_id'])
 filename = 'users_info_%s.xlsx' % random.randint(0, 100)
 writer = pandas.ExcelWriter(filename, engine='xlsxwriter')
 df.to_excel(writer, sheet_name='user information')

#pandas #python #excel #dataframe

Machine learning full course using python.

#tensorflow #machine_learning #python #numpy #pandas

PyData Amsterdam 2017

This talk gives an overview of the Neo4j graph database and the Cypher query language from the point of view of a Python user. We'll look at how to run queries and visualise or extract those results into software such as Pandas. We'll also explore the property graph data model and look at how it differs from other data models.

Graph databases offer a fresh perspective on data modelling and one that is often closer to the real world than a traditional RDBMS. In this talk, we'll look at how to work with Neo4j's property graph data model from the point of view of a Python user, how this model differs from other database models and we'll also show how to integrate the Cypher query language into a Python application.

This talk will (hopefully!) contain a couple of live demonstrations. We'll explore how to integrate Cypher query results with data analysis tools such as Pandas as well as how to visualise graph data through the Neo4j browser.


#neo4js #python #pandas #pyvideo

#pandas #ebook #epub

Data Analysis


Create a dataframe from dictionary in Pandas:

import pandas
 data = [{'id': 1, 'name': 'alireza'}, {'id': 2, 'name': 'Mohsen'}]
 # Creating a dataframe from a dictionary object
 df = pandas.DataFrame(data)

Now if you print dataframe:

> df
    id     name
 0   1  alireza
 1   2   Mohsen

NOTE: the first column is the index column.


In order to turn it to a dictionary after your aggregation, analysis, etc just use to_dict like below:

df.to_dict(orient='records')
 [{'id': 1, 'name': 'alireza'}, {'id': 2, 'name': 'Mohsen'}]

You are right! We didn't do anything useful on records, but the goal is to tell you how to turn dataframe to a dictionary not more.

NOTE: on older version of pandas you have to use outtype='records' rather than orient='records'.

#python #pandas #to_dict #outtype #orient #dictionary #dataframe

Write pandas dataframe into Google bigQuery:

https://pandas.pydata.org/pandas-docs/version/0.21/generated/pandas.DataFrame.to_gbq.html#pandas-dataframe-to-gbq

#pandas #bg #bigquery

How to sort data based on a column in Pandas?

You can use sort_values in order to sort data in a dataframe:

df.sort_values(['credit'], ascending=False, inplace=True)

The above sample assumes that you have a data frame called df and sort it based on user credit. The sort order is Descending (ascending=False). and it sorts in place (You don't have to copy the result into a new dataframe).

#python #pandas #dataframe #sort #inplace