CRISP DM first phase Data Understanding using Exploratory Data Analysis (EDA) (MatPlotLib and Seaborn)

Analysis of Stock Prices of 4 Information Technology majors from 1st January 2009 to 1st January 2018.
Four IT majors are:

  1. Oracle (ORCL)
  2. Microsoft (MSFT)
  3. SAP (SAP)
  4. Accenture (ACN)
Used Pandas Data Reader for fetching data from Morning Star using Remote Data Access
This exercise is just to practice data visualisations, no advice on Stock prices is provided here.

This exercise is to see how all four IT majors progressed their way from 1st January 2009 to 1st January 2018.


The very first step involved getting the data from Morning Star:

# Oracle dataframe

ORCL = data.DataReader("ORCL", 'morningstar', start, end)
ORCL_New = ORCL.reset_index(level=0)
# Microsoft dataframe
MSFT = data.DataReader("MSFT", 'morningstar', start, end)
MSFT_New = MSFT.reset_index(level=0)
# SAP dataframe
SAP = data.DataReader("SAP", 'morningstar', start, end)
SAP_New = SAP.reset_index(level=0)
# Accenture dataframe
ACN = data.DataReader("ACN", 'morningstar', start, end)
ACN_New = ACN.reset_index(level=0)

# Concat different dataframes along columns to get a single dataframe
it_stocks = pd.concat([ACN_New, MSFT_New, ORCL_New, SAP_New], axis=1, keys = tickers)

Exploratory Data Analysis phase begins here:
  1. Get the info() of the dataframe

       2. Describe() the dataframe
       3. Return the maximum and minimum Stock price of all 4 IT majors
        4. Basic EDA using Seaborn pairplot for the Returns of each Stock (percent change)
         5. Percent Change in returns output minimum, maximum, standard deviation
          6. Seaborn distplot to show Oracle Stock returns during 2017
            7. Seaborn distplot to show Microsoft Stock returns during 2009
         8. Matplotlib Line plot to show performance of all four IT majors year wise
         9. Seaborn heatmap to plot correlation between Closing Stock prices of 4 IT majors

Explored some of the capabilities of Matplotlib and Seaborn.
In next blog will cover few.

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