30 Days of Pandas in an article

Pandas is a Python Package specific for data manipulation. This package is inseparable in Data Scientist life and would not give you any harm to learn more about it.

Previously, in my LinkedIn post, I do a daily post in thirty-days where each day I post a new function and method from Pandas Package I deemed useful. Some ask me to make a summary of it and because of that, I would make an article about it here.

Keep Reading!

Day 1: style.bar

I want to showcase a method from Pandas Data Frame Object calledstyle.bar which allowed you to create a barplot of numerical column inside your Data Frame. You only need to call this method by using the .style.bar after the Data Frame object.

This method is useful if you want to give more impact to your data presentation and specify your point more clearly

#Day 1 style.bar

import pandas as pd
import seaborn as sns
planets = sns.load_dataset('planets')
planets.head(10)[['method','year', 'distance']].sort_values(by = 'distance').style.bar(color = 'lightblue',                                                                                  subset = 'distance').hide_index()

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#data-science #python #artificial-intelligence #towards-data-science #education

What is GEEK

Buddha Community

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

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)

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How to create a dataframe by passing a numpy array?

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

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

Matteo Powlowski

1616050935

WORKING WITH GROUPBY IN PANDAS

In my last post, I mentioned the groupby technique in Pandas library. After creating a groupby object, it is limited to make calculations on grouped data using groupby’s own functions. For example, in the last lesson, we were able to use a few functions such as mean or sum on the object we created with groupby. But with the aggregate () method, we can use both the functions we have written and the methods used with groupby. I will show how to work with groupby in this post.

#pandas-groupby #python-pandas #pandas #data-preprocessing #pandas-tutorial

Percival Schuppe

1616395265

Reading and Writing Data in Pandas

In my last post, I mentioned summarizing and computing descriptive statistics using the Pandas library. To work with data in Pandas, it is necessary to load the data set first. Reading the data set is one of the important stages of data analysis. In this post, I will talk about reading and writing data.

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Let’s get started.