1600856640

Pandas DataFrame is one of these structures which helps us do the mathematical computation very easy. The Data frame is the two-dimensional data structure, for example, the data is aligned in the tabular fashion in rows and columns.

Pandas library is the popular Python package for data science and machine learning, and with good reason: it offers dominant, expressive, and flexible data structures that make the data manipulation and analysis effortless, among many other things.

DataFrame is a two-dimensional size-mutable, potentially composite tabular data structure with labeled axes (rows and columns).

Firstly, DataFrame can contain the following data type of data.

- The Pandas Series: a one-dimensional labeled array capable of holding any data type with axis labels or index. An example of a Series object is one column from a DataFrame.
- The NumPy
**ndarray**, which can be a record or structure. - The two-dimensional
**ndarray**using**NumPy.** - Dictionaries of one-dimensional ndarray’s, lists, dictionaries or Series.

#pandas #pandas dataframe #numpy #ndarray #python

1623927960

**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

1623370500

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:

- 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.

1624431580

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

Table of Contents

**Pandas data frame**is 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

1586702221

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

1600856640

Pandas DataFrame is one of these structures which helps us do the mathematical computation very easy. The Data frame is the two-dimensional data structure, for example, the data is aligned in the tabular fashion in rows and columns.

Pandas library is the popular Python package for data science and machine learning, and with good reason: it offers dominant, expressive, and flexible data structures that make the data manipulation and analysis effortless, among many other things.

DataFrame is a two-dimensional size-mutable, potentially composite tabular data structure with labeled axes (rows and columns).

Firstly, DataFrame can contain the following data type of data.

- The Pandas Series: a one-dimensional labeled array capable of holding any data type with axis labels or index. An example of a Series object is one column from a DataFrame.
- The NumPy
**ndarray**, which can be a record or structure. - The two-dimensional
**ndarray**using**NumPy.** - Dictionaries of one-dimensional ndarray’s, lists, dictionaries or Series.

#pandas #pandas dataframe #numpy #ndarray #python