Pandas Dataframe.assign() method assigns new columns to a DataFrame, returning the new object (a copy) with the new columns added to the original ones. Please be careful while assigning the new columns because existing columns that are re-assigned will be overwritten.
To assign new columns to a DataFrame, use the Pandas assign() method. The assign() returns the new object with all original columns in addition to new ones. Existing columns that are re-assigned will be overwritten. The length of the newly assigned column must match the number of rows in the DataFrame.
In this tutorial, we are going to discuss different ways to add a new column to pandas data frame.
Table of Contents
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 list, dictionary, 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
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 Numpy, Pandas, Scikit-learn, Keras, 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 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.
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 map, shuffle, 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
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.
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:
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.
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
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