How to group data by time intervals in Python Pandas?

One-liners to combine Time-Series data into different intervals like based on each hour, week, or a month.

If you have ever dealt with Time-Series data analysis, you would have come across these problems for sure —

  1. Combining data into certain intervals like based on each day, a week, or a month.
  2. Aggregating data in the time interval like if you are dealing with price data then problems like total amount added in an hour, or a day.
  3. Finding patterns for other features in the dataset based on a time interval.

In this article, you will learn about how you can solve these problems with just one-line of code using only 2 different Pandas API’s i.e. resample() and Grouper().

As we know, the best way to learn something is to start applying it. So, I am going to use a sample time-series dataset provided by World Bank Open data and is related to the crowd-sourced price data collected from 15 countries. For more details about the data, refer Crowdsourced Price Data Collection Pilot. For this exercise, we are going to use data collected for Argentina.

#programming #data-science #pandas #python #data-analysis

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How to group data by time intervals in Python Pandas?
 iOS App Dev

iOS App Dev


Your Data Architecture: Simple Best Practices for Your Data Strategy

If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.

If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.

In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.

#big data #data science #big data analytics #data analysis #data architecture #data transformation #data platform #data strategy #cloud data platform #data acquisition

Arvel  Parker

Arvel Parker


Basic Data Types in Python | Python Web Development For Beginners

At the end of 2019, Python is one of the fastest-growing programming languages. More than 10% of developers have opted for Python development.

In the programming world, Data types play an important role. Each Variable is stored in different data types and responsible for various functions. Python had two different objects, and They are mutable and immutable objects.

Table of Contents  hide

I Mutable objects

II Immutable objects

III Built-in data types in Python

Mutable objects

The Size and declared value and its sequence of the object can able to be modified called mutable objects.

Mutable Data Types are list, dict, set, byte array

Immutable objects

The Size and declared value and its sequence of the object can able to be modified.

Immutable data types are int, float, complex, String, tuples, bytes, and frozen sets.

id() and type() is used to know the Identity and data type of the object







Built-in data types in Python

a**=str(“Hello python world”)****#str**














Numbers (int,Float,Complex)

Numbers are stored in numeric Types. when a number is assigned to a variable, Python creates Number objects.

#signed interger




Python supports 3 types of numeric data.

int (signed integers like 20, 2, 225, etc.)

float (float is used to store floating-point numbers like 9.8, 3.1444, 89.52, etc.)

complex (complex numbers like 8.94j, 4.0 + 7.3j, etc.)

A complex number contains an ordered pair, i.e., a + ib where a and b denote the real and imaginary parts respectively).


The string can be represented as the sequence of characters in the quotation marks. In python, to define strings we can use single, double, or triple quotes.

# String Handling

‘Hello Python’

#single (') Quoted String

“Hello Python”

# Double (") Quoted String

“”“Hello Python”“”

‘’‘Hello Python’‘’

# triple (‘’') (“”") Quoted String

In python, string handling is a straightforward task, and python provides various built-in functions and operators for representing strings.

The operator “+” is used to concatenate strings and “*” is used to repeat the string.


output**:****‘Hello python’**

"python "*****2

'Output : Python python ’

#python web development #data types in python #list of all python data types #python data types #python datatypes #python types #python variable type

Ray  Patel

Ray Patel


Lambda, Map, Filter functions in python

Welcome to my Blog, In this article, we will learn python lambda function, Map function, and filter function.

Lambda function in python: Lambda is a one line anonymous function and lambda takes any number of arguments but can only have one expression and python lambda syntax is

Syntax: x = lambda arguments : expression

Now i will show you some python lambda function examples:

#python #anonymous function python #filter function in python #lambda #lambda python 3 #map python #python filter #python filter lambda #python lambda #python lambda examples #python map

Kasey  Turcotte

Kasey Turcotte


How Grouping Works with Python Pandas vs R Data Table

Explained with examples

What are the average house prices in different cities of the US? What are the total sales amounts of different product groups in a store? What are the average salaries in different companies?

All these questions can be answered by using a grouping operation given that we have proper data. Most data analysis libraries and frameworks implement a function to perform such operations.

In this article, we will compare two of the most popular data analysis libraries with regards to tasks that involve grouping. The first one is Python Pandas and the other is R data table.

We will be using the Melbourne housing dataset available on Kaggle for the examples. We first import the libraries and read the dataset.

## pandas
import pandas as pd
melb = pd.read_csv("/content/melb_data.csv")

## data.table
melb <- fread("datasets/melb_data.csv")

#python #data-science #r #how grouping works with python pandas vs r data table #grouping works #data table

Kasey  Turcotte

Kasey Turcotte


400x times faster Pandas Data Frame Iteration

Avoid using iterrows() function

Data processing is and data wrangling is one of the important components of a data science model development pipeline. A data scientist spends 80% of their time preparing the dataset to make it fit for modeling. Sometimes performing data wrangling and explorations for a large-sized dataset becomes a tedious task, and one is only left to either wait quite long till the computations are completed or shift to some parallel processing.

Pandas is one of the famous Python libraries that has a vast list of API, but when it comes to scalability, it fails miserably. For large-size datasets, it takes a lot of time sometimes even hours just to iterate over the loops, and even for small-size datasets, iterating over the data frame using standard loops is quite time-consuming,

In this article, we will discuss techniques or hacks to speed the iteration process over large size datasets.

(Image by Author), Time constraints comparison to iterate over the data frame

#data-science #python #education #faster pandas #pandas data frame #400x times faster pandas data frame iteration