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Latest picks:

How to avoid a Pandas pandemonium, Part I by

Pamela Wu

Increasing the amount and diversity of data using scikit-image in Python by

Parul Pandey

A Guide to Git for Data Scientists by

Bobby Lindsey

A Simple MLOps Pipeline on Your Local Machine by

Kyle Gallatin

In case you missed them:

Why does deep learning work so well? by

Samuel Flender

What is Model Governance? by

Richard Farnworth

Data quality dimensions in IBM Watson Knowledge Catalog by

Yannick Saillet

Clustering Analysis of Countries using INFORM risk and COVID-19 data by

Catherine Lopes Ph.D.

#machine-learning #the-daily-pick #towards-data-science #data-science #editors-pick

1622536080

Latest picks:

How to avoid a Pandas pandemonium, Part I by

Pamela Wu

Increasing the amount and diversity of data using scikit-image in Python by

Parul Pandey

A Guide to Git for Data Scientists by

Bobby Lindsey

A Simple MLOps Pipeline on Your Local Machine by

Kyle Gallatin

In case you missed them:

Why does deep learning work so well? by

Samuel Flender

What is Model Governance? by

Richard Farnworth

Data quality dimensions in IBM Watson Knowledge Catalog by

Yannick Saillet

Clustering Analysis of Countries using INFORM risk and COVID-19 data by

Catherine Lopes Ph.D.

#machine-learning #the-daily-pick #towards-data-science #data-science #editors-pick

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

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 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

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When you first start out using Pandas, it’s often best to just get your feet wet and deal with problems as they come up. Then, the years pass, the amazing things you’ve been able to build with it start to accumulate, but you have a vague inkling that you keep making the same kinds of mistakes and that your code is running really slowly for what seems like pretty simple operations. This is when it’s time to dig into the inner workings of Pandas and take your code to the next level. Like with any library, the best way to optimize your code is to understand what’s going on underneath the syntax.

First in **Part I**, we’re going to eat our vegetables and cover writing clean code and spotting common silent failures. Then in **Part II**, we’ll get to speeding up your runtime and lowering your memory footprint.

I also made a Jupyter notebook with the whole lesson, both parts included.

#pandas #pandemonium

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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.

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

print(s)

**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

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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