Exploratory Data Analysis in Python using pandas
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In this video, I will be showing you how to perform basic data pre-processing and exploratory data analysis (EDA) in Python using the pandas library. For this tutorial, we will be performing exploratory data analysis to answer practical questions using the NBA Basketball player stats data that we had previously obtained via web scraping.
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I just finished watching this video. Thanks a lot for explaining each and every step so beautifully.
Hi prof, can I work with 2 dataframes in pandas like a vlookup in Excel? ie, i want to fill a column in df1 with a search criteria match on df2.
Thank you!
thank you sir
Poor dataset choice.
Very nice video. A good quick intro to Pandas for beginners and a good "refresh" for rusty users. Useful and clear. Kudos!
Thanks for the video Prof, much appreciated
good one
That was amazing!
Sir,
It's very helpful and impressive tutorial of EDA. I really amazed by your style of presentation in Google Colab. Thank You π β€οΈ
Hi Teacher, Do you have plans to make a video tutorial about de DASH?
soooo good .
This is the best comprehensive EDA intro. Hope you keep making more. Professor.
please consider for next times either using a bigger font sizes or zoom in the screen π not everyone is watching these videos at 40" screens π
Awesome Explanation as Always!! Professor!!π
getting error below
Traceback (most recent call last)
<ipython-input-10-c13fc7d20dd1> in <module>()
—-> 1 profile = ProfileReport(Iris, title="Pandas Profiling Report")
~Anaconda3libsite-packagespandas_profiling__init__.py in __init__(self, df, **kwargs)
66
67 # Get dataset statistics
—> 68 description_set = describe_df(df)
69
70 # Get sample
~Anaconda3libsite-packagespandas_profilingmodeldescribe.py in describe(df)
549
550 # Get correlations
–> 551 correlations = calculate_correlations(df, variables)
552
553 # Check correlations between numerical variables
~Anaconda3libsite-packagespandas_profilingmodelcorrelations.py in calculate_correlations(df, variables)
191 # Get the Phi_k sorted order
192 current_order = (
–> 193 correlations["phi_k"].index.get_level_values("var1").tolist()
194 )
195
~Anaconda3libsite-packagespandascoreindexesbase.py in _get_level_values(self, level)
3169 """
3170
-> 3171 self._validate_index_level(level)
3172 return self
3173
~Anaconda3libsite-packagespandascoreindexesbase.py in _validate_index_level(self, level)
1956 elif level != self.name:
1957 raise KeyError('Level %s must be same as name (%s)' %
-> 1958 (level, self.name))
1959
1960 def _get_level_number(self, level):
KeyError: 'Level var1 must be same as name (None)'
It's a great video. I have seen the best exploratory data analysis video in youtube. Please add examples with more complex data.
kindly zoom in when you are showing the code. apart from that nice video as always
Thank you for such a beautiful, step-wise & lucid explanation. One of the best videos on EDA.
Love it…
Easily explained , Thanks Professor
Awesome video & channel!! I'm a junior undergrad student in physics and data science π
As usual…. Another great video…