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This article mainly introduces how to draw time series data in Matplotlib, has a certain reference value, interested friends can refer to, I hope you can learn a lot after reading this article, the following let the editor take you to understand it.

Background introduction

You will learn how to draw time series data in Matplotlib. Time series data consists of data containing dates. For example, map the price trend of bitcoin over the past few weeks. We will learn how to format dates in different ways so that they can be better used with our graphics. Let's get started.

Getting started example

First, let's take a look at a basic time series diagram and how to format the date display of the x-axis:

From datetime import datetime,timedeltafrom matplotlib import pyplot as pltfrom matplotlib import dates as mpl_dates# sets chart style plt.style.use ('seaborn') # read data dates_x = [datetime (20119 seaborn') # read data dates_x = [datetime (20119 beacon 7 mine24), datetime (20119 pencycline 7 mine25), datetime (20119 Leife7 mine26), datetime (20119 Leife7 27), datetime (20119 Leife7 28), datetime (2019p729), datetime (2019YOBE30) # Longitudinal axis data y list y = [0L1 mine3 mine5 mine78 9] # draw time series chart plt.plot_date (dates_x,y,lineStyle='solid') # format x-axis date display plt.gcf (). Autofmt_xdate () # specify the format of display date_format = mpl_dates.DateFormatter ('% m hand% d hand% Y') plt.gca (). Xaxis.set_major_formatter (date_format) plt.tight_layout () plt.show ()

Running result:

Comprehensive example

We read from a data file data.csv data on the price and closing price of bitcoin over the past period of time, which is roughly as follows:

Implementation: import pandas as pdfrom datetime import datetime Timedeltafrom matplotlib import pyplot as pltfrom matplotlib import dates as mpl_datesfrom matplotlib import font_managerfrom pandas.plotting import\ register_matplotlib_convertersregister_matplotlib_converters () # set chart style plt.style.use ('seaborn') # read data data = pd.read_csv (' data.csv') # date in the data in csv file is str type # here processing is convenient to display the chart # can display data ['Date'] = pd.to_datetime ( Data ['Date']) data.sort_values (' Date' Inplace=True) price_date = data ['Date'] price_close = data [' Close'] # call plot_date () # display time series data chart plt.plot_date (price_date, price_close, linestyle='solid') # format x-axis date display plt.gcf (). Autofmt_xdate () zh_font = font_manager.\ FontProperties (fname='C:\\ Windows\\ Fonts\\ msyh.ttf') plt.title ('bitcoin price' Fontproperties=zh_font) plt.xlabel ('date', fontproperties=zh_font) plt.ylabel ('close', fontproperties=zh_font) plt.tight_layout () plt.show ()

Running result:

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