In [1]:
import numpy as np
import pandas as pd
In [2]:
ser = pd.Series(np.random.random(5), name = 'Column 01')
In [3]:
ser
Out[3]:
0    0.765576
1    0.213892
2    0.551984
3    0.167712
4    0.247837
Name: Column 01, dtype: float64
In [4]:
ser[2]
Out[4]:
0.5519835866233269
In [5]:
from pandas_datareader import data as wb
In [6]:
PG = wb.DataReader('PG', data_source='yahoo', start='1995-1-1')
In [7]:
PG.info()

<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 6375 entries, 1995-01-03 to 2020-04-28
Data columns (total 6 columns):
High         6375 non-null float64
Low          6375 non-null float64
Open         6375 non-null float64
Close        6375 non-null float64
Volume       6375 non-null float64
Adj Close    6375 non-null float64
dtypes: float64(6)
memory usage: 348.6 KB
In [8]:
PG.head(10)
Out[8]:
High Low Open Close Volume Adj Close
Date
1995-01-03 15.62500 15.43750 15.46875 15.59375 3318400.0 6.320252
1995-01-04 15.65625 15.31250 15.53125 15.46875 2218800.0 6.269589
1995-01-05 15.43750 15.21875 15.37500 15.25000 2319600.0 6.180927
1995-01-06 15.40625 15.15625 15.15625 15.28125 3438000.0 6.193593
1995-01-09 15.40625 15.18750 15.34375 15.21875 1795200.0 6.168259
1995-01-10 15.43750 15.18750 15.28125 15.40625 4364000.0 6.244253
1995-01-11 15.59375 15.37500 15.59375 15.37500 3738400.0 6.231590
1995-01-12 15.53125 15.31250 15.37500 15.53125 3307600.0 6.294919
1995-01-13 15.84375 15.53125 15.59375 15.62500 3992800.0 6.478915
1995-01-16 15.96875 15.75000 15.90625 15.75000 3677200.0 6.530750
In [9]:
tickers = ['PG', 'MSFT', 'T', 'F', 'GE']
new_data = pd.DataFrame()
for t in tickers:
    new_data[t] = wb.DataReader(t, data_source='yahoo', start='1995-1-1')['Adj Close']
In [10]:
new_data.tail()
Out[10]:
PG MSFT T F GE
Date
2020-04-22 118.609001 173.520004 29.469999 4.77 6.43
2020-04-23 119.400002 171.419998 29.500000 4.89 6.52
2020-04-24 118.779999 174.550003 29.709999 4.87 6.26
2020-04-27 117.449997 174.050003 30.540001 5.17 6.43
2020-04-28 116.889999 169.809998 30.650000 5.38 6.80
In [11]:
import quandl
In [12]:
mydata_01 = quandl.get('FRED/GDP')
In [13]:
mydata_01.head()
Out[13]:
Value
Date
1947-01-01 243.164
1947-04-01 245.968
1947-07-01 249.585
1947-10-01 259.745
1948-01-01 265.742
In [14]:
mydata_01.tail()
Out[14]:
Value
Date
2018-10-01 20897.804
2019-01-01 21098.827
2019-04-01 21340.267
2019-07-01 21542.540
2019-10-01 21729.124