Mya  Lynch

Mya Lynch

1599249420

Using Logical Comparisons With Pandas DataFrames

Logical comparisons are used everywhere.

The Pandas library gives you a lot of different ways that you can compare a DataFrame or Series to other Pandas objects, lists, scalar values, and more. The traditional comparison operators (<, >, <=, >=, ==, !=) can be used to compare a DataFrame to another set of values.

However, you can also use wrappers for more flexibility in your logical comparison operations. These wrappers allow you to specify the axis for comparison, so you can choose t to perform the comparison at the row or column level. Also, if you are working with a MultiIndex, you may specify which index you want to work with.

In this piece, we’ll first take a quick look at logical comparisons with the standard operators. After that, we’ll go through five different examples of how you can use these logical comparison wrappers to process and better understand your data.

The data used in this piece is sourced from Yahoo Finance. We’ll be using a subset of Tesla stock price data. Run the code below if you want to follow along. (And if you’re curious as to the function I used to get the data scroll to the very bottom and click on the first link.)

import pandas as pd

## fixed data so sample data will stay the same
df = pd.read_html("https://finance.yahoo.com/quote/TSLA/history?period1=1277942400&period2=1594857600&interval=1d&filter=history&frequency=1d")[0]
df = df.head(10) ## only work with the first 10 points

Image for post

Tesla stock data from Yahoo Finance

Logical Comparisons With Pandas

The wrappers available for use are:

  • eq (equivalent to ==) — equals to
  • ne (equivalent to !=) — not equals to
  • le (equivalent to <=) — less than or equals to
  • lt (equivalent to <) — less than
  • ge (equivalent to >=) — greater than or equals to
  • gt (equivalent to >) — greater than

#python #programming #pandas #data-science #technology

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Using Logical Comparisons With Pandas DataFrames
Kasey  Turcotte

Kasey Turcotte

1623927960

Pandas DataFrame vs. Spark DataFrame: When Parallel Computing Matters

With Performance Comparison Analysis and Guided Example of Animated 3D Wireframe Plot

Python is famous for its vast selection of libraries and resources from the open-source community. As a Data Analyst/Engineer/Scientist, one might be familiar with popular packages such as NumpyPandasScikit-learnKeras, and TensorFlow. Together these modules help us extract value out of data and propels the field of analytics. As data continue to become larger and more complex, one other element to consider is a framework dedicated to processing Big Data, such as Apache Spark. In this article, I will demonstrate the capabilities of distributed/cluster computing and present a comparison between the Pandas DataFrame and Spark DataFrame. My hope is to provide more conviction on choosing the right implementation.

Pandas DataFrame

Pandas has become very popular for its ease of use. It utilizes DataFrames to present data in tabular format like a spreadsheet with rows and columns. Importantly, it has very intuitive methods to perform common analytical tasks and a relatively flat learning curve. It loads all of the data into memory on a single machine (one node) for rapid execution. While the Pandas DataFrame has proven to be tremendously powerful in manipulating data, it does have its limits. With data growing at an exponentially rate, complex data processing becomes expensive to handle and causes performance degradation. These operations require parallelization and distributed computing, which the Pandas DataFrame does not support.

Introducing Cluster/Distribution Computing and Spark DataFrame

Apache Spark is an open-source cluster computing framework. With cluster computing, data processing is distributed and performed in parallel by multiple nodes. This is recognized as the MapReduce framework because the division of labor can usually be characterized by sets of the mapshuffle, and reduce operations found in functional programming. Spark’s implementation of cluster computing is unique because processes 1) are executed in-memory and 2) build up a query plan which does not execute until necessary (known as lazy execution). Although Spark’s cluster computing framework has a broad range of utility, we only look at the Spark DataFrame for the purpose of this article. Similar to those found in Pandas, the Spark DataFrame has intuitive APIs, making it easy to implement.

#pandas dataframe vs. spark dataframe: when parallel computing matters #pandas #pandas dataframe #pandas dataframe vs. spark dataframe #spark #when parallel computing matters

Practice Problems: How To Join DataFrames in Pandas

Hey - Nick here! This page is a free excerpt from my $199 course Python for Finance, which is 50% off for the next 50 students.

If you want the full course, click here to sign up.

It’s now time for some practice problems! See below for details on how to proceed.

Course Repository & Practice Problems

All of the code for this course’s practice problems can be found in this GitHub repository.

There are two options that you can use to complete the practice problems:

  • Open them in your browser with a platform called Binder using this link (recommended)
  • Download the repository to your local computer and open them in a Jupyter Notebook using Anaconda (a bit more tedious)

Note that binder can take up to a minute to load the repository, so please be patient.

Within that repository, there is a folder called starter-files and a folder called finished-files. You should open the appropriate practice problems within the starter-files folder and only consult the corresponding file in the finished-files folder if you get stuck.

The repository is public, which means that you can suggest changes using a pull request later in this course if you’d like.

#dataframes #pandas #practice problems: how to join dataframes in pandas #how to join dataframes in pandas #practice #/pandas/issues.

Practice Problems: How To Use Pandas DataFrames' GroupBy Method

It’s now time for some practice problems! See below for details on how to proceed.

Course Repository & Practice Problems

All of the code for this course’s practice problems can be found in this GitHub repository.

There are two options that you can use to complete the practice problems:

  • Open them in your browser with a platform called Binder using this link (recommended)
  • Download the repository to your local computer and open them in a Jupyter Notebook using Anaconda (a bit more tedious)

Note that binder can take up to a minute to load the repository, so please be patient.

Within that repository, there is a folder called starter-files and a folder called finished-files. You should open the appropriate practice problems within the starter-files folder and only consult the corresponding file in the finished-files folder if you get stuck.

The repository is public, which means that you can suggest changes using a pull request later in this course if you’d like.

#pandas #groupby methods #pandas dataframe #example #practice problems: how to use pandas dataframes' groupby method #practice problems

Paula  Hall

Paula Hall

1624431580

How to add a new column to Pandas DataFrame?

In this tutorial, we are going to discuss different ways to add a new column to pandas data frame.


Table of Contents

What is a pandas data frame?

Pandas data frameis a two-dimensional heterogeneous data structure that stores the data in a tabular form with labeled indexes i.e. rows and columns.

Usually, data frames are used when we have to deal with a large dataset, then we can simply see the summary of that large dataset by loading it into a pandas data frame and see the summary of the data frame.

In the real-world scenario, a pandas data frame is created by loading the datasets from an existing CSV file, Excel file, etc.

But pandas data frame can be also created from the listdictionary, list of lists, list of dictionaries, dictionary of ndarray/lists, etc. Before we start discussing how to add a new column to an existing data frame we require a pandas data frame.

#pandas #dataframe #pandas dataframe #column #add a new column #how to add a new column to pandas dataframe

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