Vaex: Pandas but 1000x faster

While working with pandas, if you have encountered a large dataset, then you might have thought of an alternative, especially when your machine is not strong. Pandas is really good for small/average-sized datasets, but as data gets bigger, it does not perform as well as it performs on simple and smaller datasets.

Here you can see the comparison of Pandas with another library modin on reading the dataset from a CSV file.

Similarly, a very common problem pandas users often go through is the dead jupyter kernel due to out of memory. The computations are expensive, and the CPU is not strong enough to handle those.

In this article, you are going to learn about Vaex, a Python library that is similar to Pandas, how to install it, and some of its important functions that can help you in performing different tasks.

Introduction to Vaex

Vaex is a python library that is an out-of-core dataframe, which can handle up to 1 billion rows per second. 1 billion rows. Yes, you read it right, that too, in a second. It uses memory mapping, a zero-copy policy which means that it will not touch or make a copy of the dataset unless explicitly asked to. This makes it possible to work with datasets that are equal to the size of your hard drive. Vaex also uses lazy computations for the best performance and no memory wastage.

#2021 may tutorials # overviews #big data #data preprocessing #pandas #scalability #vaex

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Vaex: Pandas but 1000x faster

Vaex: Pandas but 1000x faster

While working with pandas, if you have encountered a large dataset, then you might have thought of an alternative, especially when your machine is not strong. Pandas is really good for small/average-sized datasets, but as data gets bigger, it does not perform as well as it performs on simple and smaller datasets.

Here you can see the comparison of Pandas with another library modin on reading the dataset from a CSV file.

Similarly, a very common problem pandas users often go through is the dead jupyter kernel due to out of memory. The computations are expensive, and the CPU is not strong enough to handle those.

In this article, you are going to learn about Vaex, a Python library that is similar to Pandas, how to install it, and some of its important functions that can help you in performing different tasks.

Introduction to Vaex

Vaex is a python library that is an out-of-core dataframe, which can handle up to 1 billion rows per second. 1 billion rows. Yes, you read it right, that too, in a second. It uses memory mapping, a zero-copy policy which means that it will not touch or make a copy of the dataset unless explicitly asked to. This makes it possible to work with datasets that are equal to the size of your hard drive. Vaex also uses lazy computations for the best performance and no memory wastage.

#2021 may tutorials # overviews #big data #data preprocessing #pandas #scalability #vaex

Kasey  Turcotte

Kasey Turcotte

1623992040

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

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)

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

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