In [4]:
data_frame.plot(x="date", y="price", figsize = (10, 6))
Out[4]:
<AxesSubplot:xlabel='date'>
During initial setup we configure matplotlib for visualizations, load the data into a data frame using pandas and show the first few records.
%matplotlib inline
import pandas as pd
data_frame = pd.read_csv("data.csv")
data_frame.head(10)
| date | price | open | high | low | volume | change_percent | |
|---|---|---|---|---|---|---|---|
| 0 | Nov 14, 2019 | 3,096.63 | 3,090.75 | 3,098.20 | 3,083.26 | - | 0.08% |
| 1 | Nov 13, 2019 | 3,094.04 | 3,084.18 | 3,098.06 | 3,078.80 | - | 0.07% |
| 2 | Nov 12, 2019 | 3,091.84 | 3,089.28 | 3,102.61 | 3,084.73 | - | 0.16% |
| 3 | Nov 11, 2019 | 3,087.01 | 3,080.33 | 3,088.33 | 3,075.82 | - | -0.20% |
| 4 | Nov 08, 2019 | 3,093.08 | 3,081.25 | 3,093.09 | 3,073.58 | - | 0.26% |
| 5 | Nov 07, 2019 | 3,085.18 | 3,087.02 | 3,097.77 | 3,080.23 | - | 0.27% |
| 6 | Nov 06, 2019 | 3,076.78 | 3,075.10 | 3,078.34 | 3,065.89 | - | 0.07% |
| 7 | Nov 05, 2019 | 3,074.62 | 3,080.80 | 3,083.95 | 3,072.15 | - | -0.12% |
| 8 | Nov 04, 2019 | 3,078.27 | 3,078.96 | 3,085.20 | 3,074.87 | - | 0.37% |
| 9 | Nov 01, 2019 | 3,066.91 | 3,050.72 | 3,066.95 | 3,050.72 | - | 0.97% |
Now that we have a basic idea of how the data is structured lets get more info on the data frame to get an idea of data size, typing, etc.
data_frame.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 5000 entries, 0 to 4999 Data columns (total 7 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 date 5000 non-null object 1 price 5000 non-null object 2 open 5000 non-null object 3 high 5000 non-null object 4 low 5000 non-null object 5 volume 5000 non-null object 6 change_percent 5000 non-null object dtypes: object(7) memory usage: 273.6+ KB
Let's clean up the data frame a bit so we are focused only on the data we care about.
data_frame['date'] = pd.to_datetime(data_frame.date)
data_frame['price'] = pd.to_numeric(data_frame["price"].astype(str).str.replace(",",""))
data_frame = data_frame.drop(['volume', 'change_percent'], axis = 1, errors = 'ignore')
data_frame.set_index("date")
data_frame.head(10)
| date | price | open | high | low | |
|---|---|---|---|---|---|
| 0 | 2019-11-14 | 3096.63 | 3,090.75 | 3,098.20 | 3,083.26 |
| 1 | 2019-11-13 | 3094.04 | 3,084.18 | 3,098.06 | 3,078.80 |
| 2 | 2019-11-12 | 3091.84 | 3,089.28 | 3,102.61 | 3,084.73 |
| 3 | 2019-11-11 | 3087.01 | 3,080.33 | 3,088.33 | 3,075.82 |
| 4 | 2019-11-08 | 3093.08 | 3,081.25 | 3,093.09 | 3,073.58 |
| 5 | 2019-11-07 | 3085.18 | 3,087.02 | 3,097.77 | 3,080.23 |
| 6 | 2019-11-06 | 3076.78 | 3,075.10 | 3,078.34 | 3,065.89 |
| 7 | 2019-11-05 | 3074.62 | 3,080.80 | 3,083.95 | 3,072.15 |
| 8 | 2019-11-04 | 3078.27 | 3,078.96 | 3,085.20 | 3,074.87 |
| 9 | 2019-11-01 | 3066.91 | 3,050.72 | 3,066.95 | 3,050.72 |
data_frame.plot(x="date", y="price", figsize = (10, 6))
<AxesSubplot:xlabel='date'>