/Exploratory-Data-Analysis-for-Financial-Data

Primary LanguageJupyter Notebook

Exploratory-Data-Analysis-for-Financial-Data

Description

In this jupyter notebook, I have performed Exploratory Data Analysis on Financial data.

I have used stocks data of a company (named any XYZ) which contains the opening, closing, high and low stock prices per day.

Also performed data filtering and built some visualizations from the dataset.

Contents

  • Importing the required libraries

    • pandas : pandas is a fast, powerful, flexible and easy to use open source data analysis and manipulation tool, built on top of the Python programming language.
    • numpy : NumPy is a Python library that provides a simple yet powerful data structure: the n-dimensional array.
    • matplotlib : Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations in Python.
    • seaborn : Seaborn is a Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics.

  • Knowing the dataset

Analyzing historical price data is an important way to try to make future predictions. We will be using a dataset of stock data for the company XYZ. This data includes opening, closing, high, low prices and stocks traded per day.

  • Importing and Loading the dataset

    • The dataset is available in the repository.(Here)
    • Dataset: HistoricalQuotes.csv

  • Exploratory Data Analysis

    • df.shape
    • df.info()
    • df.head()
    • df.isna().sum()
    • df.drop()
      • df.drop(df.index[0], inplace=True) : Dropping 1st row of the dataset
    • Changing the dtype of the open,close,high and low columns to float
      • df.open = df.open.astype(float)
    • Changing the dtype of the volume column to int
      • df.volume = pd.to_numeric(df['volume']).astype(int)
    • df.describe() - Summary statistics for all numeric columns (default)
      • df.describe(include='int') : Summary statistics only for columns whose type is "int"
    • Percentiles
      • df.describe(percentiles=[.3, .5, .9]) : Summary statistics for all numeric columns with percentiles .3, .5 and .9

  • Filtering Data

    • Creating different dataframes using masks.
      • For example: Create a mask for all of the rows whose daily high is greater than $600.
      • high_mask = df.high > 600; df.loc[high_mask]

  • Filtering Data with dates

    • Selecting date from date ranges
      • start_date = datetime(2019,8,2); end_date = datetime(2019,7,29)
    • Importing datetime package.
      • from datetime import datetime
    • Creating mask of historical dates.
      • mask = (end_date <= df.index) & (df.index <= start_date); df2 = df[mask]; df2

  • Data Visualizations

    • Line Plot showing Daily High Prices
    • Histogram of High Prices
    • Line plot of Volume over time
    • Daily Volume Frequency distribution
    • Filtering & Plotting data
      • Filtering data of year 2016 from the dataset and then plotting it on a line plot over time
      • df.loc['2016'].plot(y='low'); plt.show()