Pandas to Look for the Cutest Cat Per Town

Hi, in the last article we looked at solving the typical greatest-n-per-group using SQL and Elasticsearch. In this article will explore how to solve it using a powerful Python data analysis library, Pandas. 🐼
Pandas got two important data structures Series and DataFrame. We will be exploring the Pandas DataFrame data structure and some of its functions to solve our problem. Talking about the problem, let me explain it if you haven’t read the previous article. In a town, there would be many cute cats. We have to pick only one cutest cat from each town, whose name is Meow (or similar to Meow, will see it later).

#pandas #python #coding #programming #challenge

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Pandas to Look for the Cutest Cat Per Town

Pandas to Look for the Cutest Cat Per Town

Hi, in the last article we looked at solving the typical greatest-n-per-group using SQL and Elasticsearch. In this article will explore how to solve it using a powerful Python data analysis library, Pandas. 🐼
Pandas got two important data structures Series and DataFrame. We will be exploring the Pandas DataFrame data structure and some of its functions to solve our problem. Talking about the problem, let me explain it if you haven’t read the previous article. In a town, there would be many cute cats. We have to pick only one cutest cat from each town, whose name is Meow (or similar to Meow, will see it later).

#pandas #python #coding #programming #challenge

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

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?

  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

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

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.

Before starting the topic, our Medium page includes posts on data science, artificial intelligence, machine learning, and deep learning. Please don’t forget to follow us on Medium 🌱 to see these posts and the latest posts.

Let’s get started.

#python-pandas-tutorial #pandas-read #pandas #python-pandas