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**DataFrame** and **Series** are two core data structures in Pandas. **DataFrame** is a 2-dimensional labeled data with rows and columns. It is like a spreadsheet or SQL table. **Series** is a 1-dimensional labeled array. It is sort of like a more powerful version of the Python **list**. Understanding Series is very important, not only because it is one of the core data structures, but also because it is the building blocks of a DataFrame.

In this article, you’ll learn the most commonly used data operations with Pandas **Series **and should help you get started with Pandas. The article is structured as follows:

- Creating a Series
- Retrieving elements
- Attributes (commonly used)
- Methods (commonly used)
- Working with Python built-in functions

#data-science #python #data-analysis #series #pandas

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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.

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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

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Handling NaN in Series is Mandatory to learn to start with handling the Missing Data in field of Data Analytics … Let’s explore the same…

#python #pandas #programming #pandas-series #pandas.series #nan

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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.

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

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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.

s = pd.Series([1,2,3,4,56,np.nan,7,8,90])

print(s)

**How to create a dataframe by passing a numpy array?**

- d= pd.date_range(‘20200809’,periods=15)
- print(d)
- df = pd.DataFrame(np.random.randn(15,4), index= d, columns = [‘A’,’B’,’C’,’D’])
- print(df)

#pandas-series #pandas #pandas-in-python #pandas-dataframe #python