In [12]:
df['Open'].hist(bins=10, range=(0, 25000));
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
df = pd.read_csv('BTC-USD.csv')
print(df.head(5))
Date Open High Low Close Adj Close \
0 2014-10-01 387.427002 411.697998 289.295990 338.321014 338.321014
1 2014-11-01 338.649994 457.092987 320.626007 378.046997 378.046997
2 2014-12-01 378.248993 384.037994 304.231995 320.192993 320.192993
3 2015-01-01 320.434998 320.434998 171.509995 217.464005 217.464005
4 2015-02-01 216.867004 265.610992 212.014999 254.263000 254.263000
Volume
0 902994450
1 659733360
2 553102310
3 1098811912
4 711518700
df.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 99 entries, 0 to 98 Data columns (total 7 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Date 99 non-null object 1 Open 99 non-null float64 2 High 99 non-null float64 3 Low 99 non-null float64 4 Close 99 non-null float64 5 Adj Close 99 non-null float64 6 Volume 99 non-null int64 dtypes: float64(5), int64(1), object(1) memory usage: 5.5+ KB
df['Date'] = pd.to_datetime(df['Date'], format='%Y-%m-%d')
df.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 99 entries, 0 to 98 Data columns (total 7 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Date 99 non-null datetime64[ns] 1 Open 99 non-null float64 2 High 99 non-null float64 3 Low 99 non-null float64 4 Close 99 non-null float64 5 Adj Close 99 non-null float64 6 Volume 99 non-null int64 dtypes: datetime64[ns](1), float64(5), int64(1) memory usage: 5.5 KB
print(len(df[df['Date'].isna()]))
0
df['Open'].describe()
count 99.000000 mean 12801.544467 std 16185.720294 min 216.867004 25% 686.925995 50% 7044.810059 75% 15640.101074 max 61320.449219 Name: Open, dtype: float64
df['High'].describe()
count 99.000000 mean 15238.411850 std 18922.389644 min 247.804001 25% 767.113495 50% 9008.314453 75% 19919.131836 max 68789.625000 Name: High, dtype: float64
df['Low'].describe()
count 99.000000 mean 10696.337298 std 13336.132579 min 171.509995 25% 644.994995 50% 6136.419922 75% 14421.103515 max 53569.765625 Name: Low, dtype: float64
df['Close'].describe()
count 99.000000 mean 12968.641746 std 16148.108009 min 217.464005 25% 723.331482 50% 7193.599121 75% 16885.576172 max 61318.957031 Name: Close, dtype: float64
df['Adj Close'].describe()
count 99.000000 mean 12968.641746 std 16148.108009 min 217.464005 25% 723.331482 50% 7193.599121 75% 16885.576172 max 61318.957031 Name: Adj Close, dtype: float64
df['Volume'].describe()
count 9.900000e+01 mean 4.997725e+11 std 5.564584e+11 min 5.531023e+08 25% 3.505475e+09 50% 1.991007e+11 75% 9.239591e+11 max 2.267153e+12 Name: Volume, dtype: float64
df['Open'].hist(bins=10, range=(0, 25000));
df['Close'].hist(bins=10, range=(0, 25000));
df['Low'].hist(bins=10, range=(0, 25000));
df['High'].hist(bins=10, range=(0, 25000));
df['Open'].plot();
df['Close'].plot();
df['Low'].plot();
df['High'].plot();
df['Adj Close'].plot();
df['Volume'].plot();
df.plot(x='Date', y='Open', kind='scatter');
df.plot(x='Date', y='Close', kind='scatter');
df.plot(x='Date', y='Low', kind='scatter');
df.plot(x='Date', y='High', kind='scatter');
df.plot(x='Date', y='Adj Close', kind='scatter');
df.plot(x='Date', y='Volume', kind='scatter');
df_open = df.pivot_table(index='Date', values='Open', aggfunc='mean')
df_open.plot(title="Средний показатель биткоина по года Open", style="o-", grid=True, figsize=(15,5));
df_close = df.pivot_table(index='Date', values='Close', aggfunc='mean')
df_close.plot(title="Средний показатель биткоина по года Close", style="o-", grid=True, figsize=(15,5));
df_low = df.pivot_table(index='Date', values='Low', aggfunc='mean')
df_low.plot(title="Средний показатель биткоина по года Low", style="o-", grid=True, figsize=(15,5));
df_high = df.pivot_table(index='Date', values='High', aggfunc='mean')
df_high.plot(title="Средний показатель биткоина по года High", style="o-", grid=True, figsize=(15,5));
df_Adj_Close = df.pivot_table(index='Date', values='Adj Close', aggfunc='mean')
df_Adj_Close.plot(title="Средний показатель биткоина по года Adj Close", style="o-", grid=True, figsize=(15,5));
df_volume = df.pivot_table(index='Date', values='Volume', aggfunc='mean')
df_volume.plot(title="Средний показатель биткоина по года Volume", style="o-", grid=True, figsize=(15,5));
plt.figure(figsize=(25,7))
plt.plot(df['Date'], df['Open'], label = 'Open')
plt.plot(df['Date'], df['Close'], label = 'Close')
plt.plot(df['Date'], df['High'], label = 'High')
plt.plot(df['Date'], df['Low'], label = 'Low')
plt.plot(df['Date'], df['Adj Close'], label = 'Adj Close')
plt.xticks(rotation=90)
plt.ylabel('Выручка')
plt.title('Графики')
plt.legend()
plt.show()
plt.figure(figsize=(25,10))
plt.plot(df['Date'], df['Volume'], label = 'Open')
plt.xticks(rotation=90)
plt.ylabel('Выручка')
plt.title('Графики')
plt.legend()
plt.show()
