Elvis Miranda

Elvis Miranda

1552115182

pandas index_col=“datetime” makes df['datetime'] unavailable

The title says it all. The following bit of pseudo-code returns the following error:

df = pd.read_sql(query, conn, parse_dates=["datetime"],
                             index_col="datetime")
df['datetime']

I get :

Exception in thread Thread-1:
 Traceback (most recent call last):
   File "C:\Users\admin\.virtualenvs\EnkiForex-ey09TNOL\lib\site-packages\pandas\core\indexes\base.py", line 2656, in get_loc
     return self._engine.get_loc(key)
   File "pandas\_libs\index.pyx", line 108, in pandas._libs.index.IndexEngine.get_loc
   File "pandas\_libs\index.pyx", line 132, in pandas._libs.index.IndexEngine.get_loc
   File "pandas\_libs\hashtable_class_helper.pxi", line 1601, in pandas._libs.hashtable.PyObjectHashTable.get_item
   File "pandas\_libs\hashtable_class_helper.pxi", line 1608, in pandas._libs.hashtable.PyObjectHashTable.get_item
 KeyError: 'datetime'

Am I misunderstanding what's going on by indexing the datetime col? I can access all the other columns normally though.

#python

What is GEEK

Buddha Community

Sofia Kelly

1552274924

An index is not a column. Think of the index as labels for the rows of the DataFrame. index_col='datetime' makes the datetime column (in the csv) the index of df. To access the index, use df.index.

Nina Diana

1552274984

import pandas as pd
d = {'col1': [1, 2], 'col2': [3, 4]}
df = pd.DataFrame(d)

time = pd.date_range(end='4/5/2018',periods=2)
df.index = time
df.index

the end is DatetimeIndex(['2018-04-04', '2018-04-05'], dtype='datetime64[ns]', freq='D') just use df.index can get the information of the index_col

Udit Vashisht

1586702221

Python Pandas Objects - Pandas Series and Pandas Dataframe

In this post, we will learn about pandas’ data structures/objects. Pandas provide two type of data structures:-

Pandas Series

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

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

Paula  Hall

Paula Hall

1623396211

Making Pandas fast with Dask parallel computing

So you, my dear Python enthusiast, have been learning Pandas and Matplotlib for a while and have written a super cool code to analyze your data and visualize it. You are ready to run your script that reads a huge file and all of a sudden your laptop starts making un ugly noise and burning like hell. Sounds familiar?

Well, I have got a couple of good news for you: this issue doesn’t need to happen anymore and you no, you don’t need to upgrade your laptop or your server.

Introducing Dask:

Dask is a flexible library for parallel computing with Python. It provides multi-core and distributed parallel execution on larger-than-memory datasets. It figures out how to break up large computations and route parts of them efficiently onto distributed hardware.

A massive cluster is not always the right choice

Today’s laptops and workstations are surprisingly powerful and, if used correctly, can handle datasets and computations for which we previously depended on clusters. A modern laptop has a multi-core CPU, 32GB of RAM, and flash-based hard drives that can stream through data several times faster than HDDs or SSDs of even a year or two ago.

As a result, Dask can empower analysts to manipulate 100GB+ datasets on their laptop or 1TB+ datasets on a workstation without bothering with the cluster at all.

The project has been a massive plus for the Python machine learning Ecosystem because it democratizes big data analysis. Not only can you save money on bigger servers, but also it copies the Pandas API so you can run your Panda script changing very few lines of code.

#making pandas fast with dask parallel computing #dask parallel computing #pandas #pandas fast #dask #dask parallel

Oleta  Becker

Oleta Becker

1602550800

Pandas in Python

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.

How to create dataframe and series?

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

print(s)

Image for post

How to create a dataframe by passing a numpy array?

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

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

WORKING WITH GROUPBY IN PANDAS

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

How does tinder make money?

Essential information regarding how do dating apps make money and how does tinder make money. Moreover, we present unique ways to make money through dating apps.

#how does tinder make money #how does bumble make money #how much money do dating apps make #how dating apps make money #how do dating apps make money