Concatenate Data and Transform Data in Python : Tutorial 4 in Jupyter Notebook

///Concatenate Data and Transform Data in Python : Tutorial 4 in Jupyter Notebook

Concatenate Data and Transform Data in Python : Tutorial 4 in Jupyter Notebook

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Python for Data Science. Concatenation and Transforming Data in Python Jupyter Notebook.

This is the 4th Video of Python for Data Science Course! In This series I will explain to you Python and Data Science all the time! It is a deep rooted fact, Python is the best programming language for data analysis because of its libraries for manipulating, storing, and gaining understanding from data. Watch this video to learn about the language that make Python the data science powerhouse. Jupyter Notebooks have become very popular in the last few years, and for good reason. They allow you to create and share documents that contain live code, equations, visualizations and markdown text. This can all be run from directly in the browser. It is an essential tool to learn if you are getting started in Data Science, but will also have tons of benefits outside of that field. Harvard Business Review named data scientist “the sexiest job of the 21st century.” Python pandas is a commonly-used tool in the industry to easily and professionally clean, analyze, and visualize data of varying sizes and types. We’ll learn how to use pandas, Scipy, Sci-kit learn and matplotlib tools to extract meaningful insights and recommendations from real-world data sets.

Download Link for Cars Data Set:

Download Link for Enrollment Forecast:

Download Link for Iris Data Set:

Download Link for Snow Inventory:

Download Link for Super Store Sales:

Download Link for States:

Download Link for Spam-base Data Base:

Download Link for Parsed Data:

Download Link for HTML File:


By |2019-06-12T19:45:56+00:00June 12th, 2019|Python Video Tutorials|5 Comments


  1. Arjun k June 12, 2019 at 7:46 pm - Reply

    Hi Sir,

    I am getting the below error while join. Can you please help:

    ValueError Traceback (most recent call last)

    <ipython-input-38-a20ef5018a87> in <module>

    —-> 1 dataset_variable = DataFrame.join(dataset1,series_new)

    2 dataset_variable in join(self, other, on, how, lsuffix, rsuffix, sort)

    6334 # For SparseDataFrame's benefit

    6335 return self._join_compat(other, on=on, how=how, lsuffix=lsuffix,

    -> 6336 rsuffix=rsuffix, sort=sort)


    6338 def _join_compat(self, other, on=None, how='left', lsuffix='', rsuffix='', in _join_compat(self, other, on, how, lsuffix, rsuffix, sort)

    6349 return merge(self, other, left_on=on, how=how,

    6350 left_index=on is None, right_index=True,

    -> 6351 suffixes=(lsuffix, rsuffix), sort=sort)

    6352 else:

    6353 if on is not None: in merge(left, right, how, on, left_on, right_on, left_index, right_index, sort, suffixes, copy, indicator, validate)

    60 copy=copy, indicator=indicator,

    61 validate=validate)

    —> 62 return op.get_result()


    64 in get_result(self)


    573 llabels, rlabels = items_overlap_with_suffix(ldata.items, lsuf,

    –> 574 rdata.items, rsuf)


    576 lindexers = {1: left_indexer} if left_indexer is not None else {} in items_overlap_with_suffix(left, lsuffix, right, rsuffix)

    5242 if not lsuffix and not rsuffix:

    5243 raise ValueError('columns overlap but no suffix specified: '

    -> 5244 '{rename}'.format(rename=to_rename))


    5246 def lrenamer(x):

    ValueError: columns overlap but no suffix specified: Index(['My First series'], dtype='object')


  2. Hari Warshan June 12, 2019 at 7:46 pm - Reply

    Thanks you so much sir🙏..but rather than using random numbers will u show us some real time examples? It will help us a lot sir.thanks in advance

  3. Nitin Chauhan June 12, 2019 at 7:46 pm - Reply

    nice videos and nice teaching style respected sir. sir can this also used with python3

  4. Ramakrishna T June 12, 2019 at 7:46 pm - Reply

    can you please share the code also here

  5. natalie nie June 12, 2019 at 7:46 pm - Reply

    thanks a lot for this!!!! helped me a ton~~~

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