In [1]:

from datetime import datetime, timedelta
from matplotlib import pyplot as plt
from matplotlib import dates as mpl_dates
import pandas as pd

In [2]:

plt.style.use('seaborn')
plt.rc('figure', figsize=(12, 10))

In [3]:

dates = [
    datetime(2019, 5, 24),
    datetime(2019, 5, 25),
    datetime(2019, 5, 26),
    datetime(2019, 5, 27),
    datetime(2019, 5, 28),
    datetime(2019, 5, 29),
    datetime(2019, 5, 30)
]

# print(dates)
y = [0, 1, 3, 4, 6, 5, 7]

plt.plot_date(dates, y, linestyle='solid')

# to get current figure
plt.gcf().autofmt_xdate()

# change date format
dates = [x.strftime("%b, %d %Y") for x in dates]

# %b - month
# %d - day
# %Y - year

plt.gca().set_xticklabels(dates)
# plt.set_xticklabels(dates)

plt.show()

C:\Users\batman\Anaconda3\lib\site-packages\pandas\plotting\_matplotlib\converter.py:103: FutureWarning: Using an implicitly registered datetime converter for a matplotlib plotting method. The converter was registered by pandas on import. Future versions of pandas will require you to explicitly register matplotlib converters.

To register the converters:
	>>> from pandas.plotting import register_matplotlib_converters
	>>> register_matplotlib_converters()
  warnings.warn(msg, FutureWarning)

In [4]:

# example

In [5]:

data = pd.read_csv(r'examples/data_8.csv')
data.head()

Out[5]:

	Date	Open	High	Low	Close	Adj Close	Volume
0	2019-05-18	7266.080078	8281.660156	7257.259766	8193.139648	8193.139648	723011166
1	2019-05-19	8193.139648	8193.139648	7591.850098	7998.290039	7998.290039	637617163
2	2019-05-20	7998.290039	8102.319824	7807.770020	7947.930176	7947.930176	357803946
3	2019-05-21	7947.930176	8033.759766	7533.660156	7626.890137	7626.890137	424501866
4	2019-05-22	7626.890137	7971.259766	7478.740234	7876.500000	7876.500000	386766321

In [6]:

data['Date'] = pd.to_datetime(data['Date'])
data.sort_values('Date', inplace=True)

In [7]:

price_date = data['Date']
price_close = data['Close']

In [8]:

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.show()