1620147000

Saving Santa with Pandas and GeoPandas

**Finally! I know!** **A Data Science Project as Festive as the Season!**

Data Science has many applications; so many in fact, it is nigh impossible to even be considered proficient in half of them! But, that shouldn’t dissuade us from trying to become as equipped for the future as possible. I challenge you to take on this project and have a once in a lifetime opportunity to **serve as Santa’s “pocket” Data Scientist**!

*As a precursor to this project, note that this project is designed to be a challenge project. You are given the tools and supplies, but the method is left up to you! While the ‘answer key’ is provided on Github, you should have everything you need in the data to figure it out. This project also serves to introduce you to some tools you may not be familiar with, yet.*

- A
**good understanding of Data Science**oriented**Python libraries** - A
**basic understanding of GeoPandas** - A
**love**of**Christmas!**

- How to
**merge non-standardized data**with a**Universal Standard Identifier (****Placekey****)** - How to
**find points within polygons**using Geopandas - How to
**filter the data**to determine the best possible outcome (min/max) - How to
**merge data**and**determine foot traffic to given POI**(SafeGraph) - How tosave** Santa** and
**Christmas**

#christmas #data-science #gis #pandas #projects

1620147000

Saving Santa with Pandas and GeoPandas

**Finally! I know!** **A Data Science Project as Festive as the Season!**

Data Science has many applications; so many in fact, it is nigh impossible to even be considered proficient in half of them! But, that shouldn’t dissuade us from trying to become as equipped for the future as possible. I challenge you to take on this project and have a once in a lifetime opportunity to **serve as Santa’s “pocket” Data Scientist**!

*As a precursor to this project, note that this project is designed to be a challenge project. You are given the tools and supplies, but the method is left up to you! While the ‘answer key’ is provided on Github, you should have everything you need in the data to figure it out. This project also serves to introduce you to some tools you may not be familiar with, yet.*

- A
**good understanding of Data Science**oriented**Python libraries** - A
**basic understanding of GeoPandas** - A
**love**of**Christmas!**

- How to
**merge non-standardized data**with a**Universal Standard Identifier (****Placekey****)** - How to
**find points within polygons**using Geopandas - How to
**filter the data**to determine the best possible outcome (min/max) - How to
**merge data**and**determine foot traffic to given POI**(SafeGraph) - How tosave** Santa** and
**Christmas**

#christmas #data-science #gis #pandas #projects

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

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

1616395265

In my last post, I mentioned summarizing and computing descriptive statistics using the Pandas library. To work with data in Pandas, it is necessary to load the data set first. Reading the data set is one of the important stages of data analysis. In this post, I will talk about reading and writing data.

Before starting the topic, our Medium page includes posts on data science, artificial intelligence, machine learning, and deep learning. Please don’t forget to follow us on ** Medium** 🌱 to see these posts and the latest posts.

Let’s get started.

#python-pandas-tutorial #pandas-read #pandas #python-pandas