Jamison  Fisher

Jamison Fisher

1619445960

All Pandas json_normalize() You Should Know for Flattening JSON

Some of the most useful Pandas tricks

Reading data is the first step in any data science project. As a machine learning practitioner or a data scientist, you would have surely come across JSON (JavaScript Object Notation) data. JSON is a widely used format for storing and exchanging data. For example, NoSQL database like MongoDB store the data in JSON format, and REST API’s responses are mostly available in JSON.

Although this format works well for storing and exchanging data, it needs to be converted into a tabular form for further analysis. You are likely to deal with 2 types of JSON structure, a JSON object or a list of JSON objects. In internal Python lingo, you are most likely to deal with a dict or a list of dicts.

A dictionary and a list of dictionaries (Image by author)

In this article, you’ll learn how to use Pandas’s built-in function json_normalize() to flatten those 2 types of JSON into Pandas DataFrames. This article is structured as follows:

  1. Flattening a simple JSON
  2. Flattening a JSON with multiple levels
  3. Flattening a JSON with a nested list
  4. Ignoring KeyError if keys are not always present
  5. Custom separator using sep
  6. Adding prefix for meta and record data
  7. Working with a local file
  8. Working with a URL

Please check out Notebook for the source code.

#data-analysis #json #python #pandas #data-science

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All Pandas json_normalize() You Should Know for Flattening JSON
Brandon  Adams

Brandon Adams

1625637060

What is JSON? | JSON Objects and JSON Arrays | Working with JSONs Tutorial

In this video, we work with JSONs, which are a common data format for most web services (i.e. APIs). Thank you for watching and happy coding!

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#jsons #json arrays #json objects #what is json #jsons tutorial #blondiebytes

Jamison  Fisher

Jamison Fisher

1619445960

All Pandas json_normalize() You Should Know for Flattening JSON

Some of the most useful Pandas tricks

Reading data is the first step in any data science project. As a machine learning practitioner or a data scientist, you would have surely come across JSON (JavaScript Object Notation) data. JSON is a widely used format for storing and exchanging data. For example, NoSQL database like MongoDB store the data in JSON format, and REST API’s responses are mostly available in JSON.

Although this format works well for storing and exchanging data, it needs to be converted into a tabular form for further analysis. You are likely to deal with 2 types of JSON structure, a JSON object or a list of JSON objects. In internal Python lingo, you are most likely to deal with a dict or a list of dicts.

A dictionary and a list of dictionaries (Image by author)

In this article, you’ll learn how to use Pandas’s built-in function json_normalize() to flatten those 2 types of JSON into Pandas DataFrames. This article is structured as follows:

  1. Flattening a simple JSON
  2. Flattening a JSON with multiple levels
  3. Flattening a JSON with a nested list
  4. Ignoring KeyError if keys are not always present
  5. Custom separator using sep
  6. Adding prefix for meta and record data
  7. Working with a local file
  8. Working with a URL

Please check out Notebook for the source code.

#data-analysis #json #python #pandas #data-science

Udit Vashisht

1586702221

Python Pandas Objects - Pandas Series and Pandas Dataframe

In this post, we will learn about pandas’ data structures/objects. Pandas provide two type of data structures:-

Pandas Series

Pandas Series is a one dimensional indexed data, which can hold datatypes like integer, string, boolean, float, python object etc. A Pandas Series can hold only one data type at a time. The axis label of the data is called the index of the series. The labels need not to be unique but must be a hashable type. The index of the series can be integer, string and even time-series data. In general, Pandas Series is nothing but a column of an excel sheet with row index being the index of the series.

Pandas Dataframe

Pandas dataframe is a primary data structure of pandas. Pandas dataframe is a two-dimensional size mutable array with both flexible row indices and flexible column names. In general, it is just like an excel sheet or SQL table. It can also be seen as a python’s dict-like container for series objects.

#python #python-pandas #pandas-dataframe #pandas-series #pandas-tutorial

Kasey  Turcotte

Kasey Turcotte

1623137018

5 Pandas Presentation Tips You Should Know About

These tips will help you when you need to share your analysis with others

These tips will help you need to to share your analysis with others. Whether you are a Student, Data Scientist or a Ph.D. Researcher, each project ends with some kind of a report. May this be a post on Confluence, Readme on GitHub or a Scientific paper.

There is no need to copy-paste values one by one from a DataFrame to another software. Pandas with its formatting functions can convert a DataFrame to many formats.

#pandas #python #data-science #programming #5 pandas presentation tips you should know about #pandas presentation tips

Oleta  Becker

Oleta Becker

1602550800

Pandas in Python

Pandas is used for data manipulation, analysis and cleaning.

What are Data Frames and Series?

Dataframe is a two dimensional, size mutable, potentially heterogeneous tabular data.

It contains rows and columns, arithmetic operations can be applied on both rows and columns.

Series is a one dimensional label array capable of holding data of any type. It can be integer, float, string, python objects etc. Panda series is nothing but a column in an excel sheet.

How to create dataframe and series?

s = pd.Series([1,2,3,4,56,np.nan,7,8,90])

print(s)

Image for post

How to create a dataframe by passing a numpy array?

  1. d= pd.date_range(‘20200809’,periods=15)
  2. print(d)
  3. df = pd.DataFrame(np.random.randn(15,4), index= d, columns = [‘A’,’B’,’C’,’D’])
  4. print(df)

#pandas-series #pandas #pandas-in-python #pandas-dataframe #python