1616808914

In this tutorial you will learn how to use the Pandas dataframe `.groupby()`

method and aggregator methods such as `.mean()`

and `.count()`

to quickly extract statistics from a large dataset (over 10 million rows). You will also be introduced to the Open University Learning Analytics dataset

Pandas is the most adorable and cuddly tabular data management library for Python. Once you get the hang of it its intuitive, object-oriented implementation and clever tricks to improve computational efficiency make for flexible and powerful data handling.

Pandas facilitates data mining, data processing, data cleaning, data visualization, and some basic statistical analysis on small to largish datasets.

One of Pandas’ most important analytical tools is the `.groupby()`

method for Pandas DataFrame objects. When you pass the name of a column of categorical data to the `.groupby(by='column')`

of a dataframe, the returned object will have each of the unique categories in the grouped column as the indices of the rows, the other features you did not group by as columns, and a third dimension of stacks of samples organized by the category used for the grouping. This method returns a new groupby object with one dimension more than the one that called it.

#data-science #pandas #python-pandas #pandas-groupby #data-analysis

1616050935

In my last post, I mentioned the groupby technique in Pandas library. After creating a groupby object, it is limited to make calculations on grouped data using groupby’s own functions. For example, in the last lesson, we were able to use a few functions such as mean or sum on the object we created with groupby. But with the aggregate () method, we can use both the functions we have written and the methods used with groupby. I will show how to work with groupby in this post.

#pandas-groupby #python-pandas #pandas #data-preprocessing #pandas-tutorial

1616808914

In this tutorial you will learn how to use the Pandas dataframe `.groupby()`

method and aggregator methods such as `.mean()`

and `.count()`

to quickly extract statistics from a large dataset (over 10 million rows). You will also be introduced to the Open University Learning Analytics dataset

Pandas is the most adorable and cuddly tabular data management library for Python. Once you get the hang of it its intuitive, object-oriented implementation and clever tricks to improve computational efficiency make for flexible and powerful data handling.

Pandas facilitates data mining, data processing, data cleaning, data visualization, and some basic statistical analysis on small to largish datasets.

One of Pandas’ most important analytical tools is the `.groupby()`

method for Pandas DataFrame objects. When you pass the name of a column of categorical data to the `.groupby(by='column')`

of a dataframe, the returned object will have each of the unique categories in the grouped column as the indices of the rows, the other features you did not group by as columns, and a third dimension of stacks of samples organized by the category used for the grouping. This method returns a new groupby object with one dimension more than the one that called it.

#data-science #pandas #python-pandas #pandas-groupby #data-analysis

1623897480

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

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

1602550800

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