1594234020

In this article, I’ll show you how to effectively explore indices and compute deeply nested aggregations on data indexed in ElasticSearch, using the **pandagg** library.

After an explanation of the motivations to write this library (*head to “ Let’s go” section if in a hurry*), we’ll work on the IMDB dataset and compute aggregations answering questions requiring queries with increasing complexity.

It assumes you have a basic knowledge of ElasticSearch concepts.

All concepts approached here are explained in more detail in. The github repository is availablelibrary documentation.here

ElasticSearch provides a powerful API to compute aggregated metrics on your indexed data: aggregations. One of the killer features is the ability to nest aggregations clauses, with the `aggs`

parameter available in bucket aggregations.

```
{
"per_genre": {
"terms": {"field": "genres","size": 3},
"aggs": {
"rating_average": {"avg": {"field": "rank"}},
"nb_roles_average": {"avg": {"field": "nb_roles"}
}
}
}
}
```

But if you have already tried to compute quite deeply nested queries, you might have struggled parsing the output of your query.

#python #aggregation #pandas

1623140954

The techniques for Reshaping, Grouping, and Pivoting the data

Python has turned the world just in a decade with its popularity and efficiency. Python has followed offering a reliable trend of Data Science which comprises of:

· Data Gathering

· Data Cleaning

· Machine Learning models

· Visualization of Data

Pandas is a very fundamental inbuilt library in Python uptakes a lot of the area. It is an open-source library that is easy to use, providing high efficiency and many tools used in the analysis of data for Python programming.

Pandas is an in-memory no SQL type database providing a helping hand for basic SQL constructs, statistical methods, and the capability of graphing. As it was built on top of Cython, it runs quicker along with consuming less time to access some memory within a machine.

→Pandas have a very advanced feature of carrying out some operations on the group of data frames.

→Data Frame: A 2D data that is labeled. It contains different columns and rows.

So, in this article, we’re going to have our quick eyes on some methods of grouping, reshaping, and pivoting the data.

#pandas #data-science #python #artificial-intelligence #playing with pandas library #pandas library

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

1591862993

This is the guide to get you started in the world of data science

#data-analysis #python #pandas #data-science #introducing pandas #programming

1594234020

In this article, I’ll show you how to effectively explore indices and compute deeply nested aggregations on data indexed in ElasticSearch, using the **pandagg** library.

After an explanation of the motivations to write this library (*head to “ Let’s go” section if in a hurry*), we’ll work on the IMDB dataset and compute aggregations answering questions requiring queries with increasing complexity.

It assumes you have a basic knowledge of ElasticSearch concepts.

All concepts approached here are explained in more detail in. The github repository is availablelibrary documentation.here

ElasticSearch provides a powerful API to compute aggregated metrics on your indexed data: aggregations. One of the killer features is the ability to nest aggregations clauses, with the `aggs`

parameter available in bucket aggregations.

```
{
"per_genre": {
"terms": {"field": "genres","size": 3},
"aggs": {
"rating_average": {"avg": {"field": "rank"}},
"nb_roles_average": {"avg": {"field": "nb_roles"}
}
}
}
}
```

But if you have already tried to compute quite deeply nested queries, you might have struggled parsing the output of your query.

#python #aggregation #pandas

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