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Created 3 years ago
import numpy as np, pandas as pd
import matplotlib.pyplot as plt, seaborn as sns
%matplotlib inline
import statsmodels.api as sm
from sklearn.metrics import mean_squared_error
from statsmodels.tsa.holtwinters import SimpleExpSmoothing
from statsmodels.tsa.holtwinters import ExponentialSmoothing
import warnings
warnings.filterwarnings("ignore")data = pd.read_csv("bitcoinfinal+(4).csv", header=None)
data.columns = ["Months", "Price"]
data = data.set_index("Months")
data.head()data.plot(figsize=(12,4))
plt.legend(loc="best")
plt.title("Bitcoin Price Data")
plt.show(block=False)
data.info()<class 'pandas.core.frame.DataFrame'>
Int64Index: 32 entries, 0 to 31
Data columns (total 1 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 Price 32 non-null float64
dtypes: float64(1)
memory usage: 512.0 bytes
fig = plt.subplots(figsize=(12,3))
ax = sns.boxplot(x = data.Price, whis=1.5)
plt.show()