Understanding Bitcoin Prices over Time with Python

In this blog post, we will explore how to use Python to analyze and visualize Bitcoin prices over time. This tutorial is designed for data analysts who are new to coding and want to learn how to work with time series data using libraries like Plotly and Pandas.

1. Introduction to the Code

The provided Python code helps us visualize Bitcoin prices over time using an area plot. We will fetch hourly Bitcoin price data from a remote URL, parse the date-time columns, and plot the closing prices on a graph.

%pip install plotly
%pip install pandas

import plotly.express as px
import plotly.io
import plotly.graph_objects as go
from dateutil.parser import parse
import datetime
import matplotlib.pyplot as plt
import pandas as pd

def MainCanvas():
    def AreaPlot(dataframe, x, y, title):
        # Code to create an area plot using Plotly Express
        ...

    def detect_datetime_columns(df):
        # Code to detect columns with datetime values
        ...

    def DatetimeParse(df):
        # Code to parse datetime columns using Pandas
        ...

    def BTC_Hourly_fun():
        # Code to fetch Bitcoin hourly data from URL
        ...

    BTC_Hourly = BTC_Hourly_fun()
    DatetimeParse_Out = DatetimeParse(BTC_Hourly)
    AreaPlot(DatetimeParse_Out, x='date', y='close', title='Bitcoin Prices over Time')
    return

MainCanvas()
        

The code uses Plotly Express to create an area plot, Pandas to parse date-time columns, and fetches Bitcoin hourly price data from a remote URL.

2. Explaining the Functions

Let's dive into the functions used in the code:

1. AreaPlot()

This function is responsible for creating the area plot. It takes four parameters: dataframe (the Pandas DataFrame containing the data), x (the name of the x-axis column), y (the name of the y-axis column), and title (the title of the plot).

2. detect_datetime_columns()

This function is used to detect columns in the DataFrame that contain datetime values. It utilizes the dateutil.parser library to parse the values and returns a list of columns that are recognized as datetime.

3. DatetimeParse()

This function processes the DataFrame and converts the detected datetime columns to proper Pandas datetime objects using pd.to_datetime(). This step is essential for working with time series data effectively.

4. BTC_Hourly_fun()

This function fetches the Bitcoin hourly price data from the provided URL using Pandas' pd.read_csv() function. It returns the DataFrame containing the Bitcoin price data.

By utilizing these functions, we fetch and prepare the data, and then create an area plot to visualize Bitcoin prices over time.

3. Conclusion

Congratulations! You've learned how to use Python to fetch, process, and visualize Bitcoin prices over time using Plotly and Pandas. This is just one of the many possibilities data analysts can explore with Python. Feel free to experiment further and expand your coding skills!