Solve different challenging problems to prepare yourself for data analytics interviews.

Pandas: Python for Data Analysis

In this module, we’ll try to solve a number of challenges covering different concepts related to data analytics in pandas, such as grouping, filtering, date manipulation, and more. This module will mainly test our knowledge while also helping us understand more advanced concepts. By the end of this module, we’ll have the confidence we need to land our dream job in data science.

Data Analytics Interview Prep Using Pandas

## Introduction
The ability to select specific rows and columns to access and filter data based on specific conditions are two of the key features of Pandas. 

Selection allows you to access specific rows or columns of the data by their index and/or location in the DataFrame.  If a medical dataset is indexed by Patient Insurance Number, you can access the patient whose insurance is `123` by an index lookup. You can also access the first/last patient in the DataFrame (or in any specific location).

Filtering allows you to choose a subset of the data we would like to keep. In a travel dataset, you can filter by a nation of origin, destination, or age. In a medical dataset, you can filter by age group, or health conditions. You can notice here that you are filtering based on conditions.

Similarly, in your music dataset, you can filter by country of origin of an artist. Or you could combine filters to view artists originating from the US and who have a large number of plays.

### Syntax

Filtering a `country` column by selecting only the country named `US` can be done as follows:

```python
df[df['country'] == 'US']
```


# Introduction
The ability to select specific rows and columns to access and filter data based on specific conditions are two of the key features of Pandas. 

Selection allows you to access specific rows or columns of the data by their index and/or location in the DataFrame.  If a medical dataset is indexed by Patient Insurance Number, you can access the patient whose insurance is `123` by an index lookup. You can also access the first/last patient in the DataFrame (or in any specific location).

Filtering allows you to choose a subset of the data we would like to keep. In a travel dataset, you can filter by a nation of origin, destination, or age. In a medical dataset, you can filter by age group, or health conditions. You can notice here that you are filtering based on conditions.

Similarly, in your music dataset, you can filter by country of origin of an artist. Or you could combine filters to view artists originating from the US and who have a large number of plays.

## Syntax

Filtering a `country` column by selecting only the country named `US` can be done as follows:

```python
df[df['country'] == 'US']
```


Learn how to utilize techniques for selecting subsets of data and filtering DataFrames.

Introduction

Selection / Filters

Grouping

Dates Manipulation

Apply / Map

Merge/Concat

Quizzes: Assorted Topics

Conclusion

Introducing Selection & Filters in Pandas

Introduction