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

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How to add a new column to Pandas DataFrame?
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

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.

Alec  Nikolaus

Alec Nikolaus

1596330300

Add a Column in a Pandas DataFrame Based on an If-Else Condition

When we’re doing data analysis with Python, we might sometimes want to add a column to a pandas DataFrame based on the values in other columns of the DataFrame.

Although this sounds straightforward, it can get a bit complicated if we try to do it using an if-else conditional. Thankfully, there’s a simple, great way to do this using numpy!

To learn how to use it, let’s look at a specific data analysis question. We’ve got a dataset of more than 4,000 Dataquest tweets. Do tweets with attached images get more likes and retweets? Let’s do some analysis to find out!

We’ll start by importing pandas and numpy, and loading up our dataset to see what it looks like. (If you’re not already familiar with using pandas and numpy for data analysis, check out our interactive numpy and pandas course).

import pandas as pd
import numpy as np

df = pd.read_csv('dataquest_tweets_csv.csv')
df.head()

adding a column to a dataframe in pandas step 1: baseline dataframe

We can see that our dataset contains a bit of information about each tweet, including:

  • date — the date the tweet was posted
  • time — the time of day the tweet was posted
  • tweet — the actual text of the tweet
  • mentions — any other twitter users mentioned in the tweet
  • photos — the url of any images included in the tweet
  • replies_count — the number of replies on the tweet
  • retweets_count — the number of retweets of the tweet
  • likes_count — the number of likes on the tweet

We can also see that the photos data is formatted a bit oddly.

Adding a Pandas Column with a True/False Condition Using np.where()

For our analysis, we just want to see whether tweets with images get more interactions, so we don’t actually need the image URLs. Let’s try to create a new column called hasimage that will contain Boolean values — True if the tweet included an image and False if it did not.

To accomplish this, we’ll use numpy’s built-in [where()](https://numpy.org/doc/stable/reference/generated/numpy.where.html) function. This function takes three arguments in sequence: the condition we’re testing for, the value to assign to our new column if that condition is true, and the value to assign if it is false. It looks like this:

np.where(condition, value if condition is true, value if condition is false)

In our data, we can see that tweets without images always have the value [] in the photos column. We can use information and np.where() to create our new column, hasimage, like so:

df['hasimage'] = np.where(df['photos']!= '[]', True, False)
df.head()

new column based on if-else has been added to our pandas dataframe

Above, we can see that our new column has been appended to our data set, and it has correctly marked tweets that included images as True and others as False.

#data science tutorials #add column #beginner #conditions #dataframe #if else #pandas #python #tutorial #tutorials #twitter

Paula  Hall

Paula Hall

1623397301

Check For a Substring in a Pandas DataFrame Column

Looking for strings to cut down your dataset for analysis and machine learning

The Pandas library is a comprehensive tool not only for crunching numbers but also for working with text data.

For many data analysis applications and machine learning exploration/pre-processing, you’ll want to either filter out or extract information from text data. To do so, Pandas offers a wide range of in-built methods that you can use to add, remove, and edit text columns in your DataFrames.

In this piece, let’s take a look specifically at searching for substrings in a DataFrame column. This may come in handy when you need to create a new category based on existing data (for example during feature engineering before training a machine learning model).

If you want to follow along, download the dataset here.

import pandas as pd

df = pd.read_csv('vgsales.csv')

Now let’s get started!

NOTE: we’ll be using a lot of _loc_ in this piece, so if you’re unfamiliar with that method, check out the first article linked at the very bottom of this piece.

#python #data-science #software-development #check for a substring in a pandas dataframe column #pandas dataframe column #check for a substring