Time Series Data - matplotlib series

Author: manjusv

08-Time Series/data.csv

@@ -0,0 +1,15 @@
+Date,Open,High,Low,Close,Adj Close,Volume
+2019-05-18,7266.080078,8281.660156,7257.259766,8193.139648,8193.139648,723011166
+2019-05-19,8193.139648,8193.139648,7591.850098,7998.290039,7998.290039,637617163
+2019-05-20,7998.290039,8102.319824,7807.770020,7947.930176,7947.930176,357803946
+2019-05-21,7947.930176,8033.759766,7533.660156,7626.890137,7626.890137,424501866
+2019-05-22,7626.890137,7971.259766,7478.740234,7876.500000,7876.500000,386766321
+2019-05-23,7876.500000,8165.450195,7801.569824,7996.399902,7996.399902,413162746
+2019-05-24,7996.399902,8140.819824,7948.680176,8059.129883,8059.129883,179206342
+2019-05-25,8059.129883,8779.000000,7894.529785,8726.230469,8726.230469,483663699
+2019-05-26,8726.230469,8931.530273,8668.459961,8785.169922,8785.169922,507164714
+2019-05-27,8785.169922,8818.709961,8562.200195,8718.849609,8718.849609,360752199
+2019-05-28,8718.849609,8760.480469,8444.099609,8664.559570,8664.559570,380343928
+2019-05-29,8664.559570,9065.889648,8027.209961,8276.250000,8276.250000,815525590
+2019-05-30,8276.250000,8570.780273,8116.000000,8560.080078,8560.080078,500141087
+2019-05-31,8550.629883,8576.339844,8459.650391,8504.980469,8504.980469,69915456

08-Time Series/demo.py

@@ -0,0 +1,24 @@
+from matplotlib import pyplot as plt
+from matplotlib import dates as mpl_dates
+import pandas as pd
+
+plt.style.use('seaborn')
+
+data = pd.read_csv('data.csv')
+
+data['Date'] = pd.to_datetime(data['Date'])
+data.sort_values('Date', inplace=True)
+
+price_date = data['Date']
+price_close = data['Close']
+
+plt.plot_date(price_date, price_close, linestyle='solid')
+plt.gcf().autofmt_xdate()
+
+plt.title('Bitcoin Prices')
+plt.xlabel('Date')
+plt.ylabel('Closing Price')
+plt.tight_layout()
+
+plt.savefig('plot.png')
+plt.show()