Explanation:This is a SHORT introduction to pandas, geared mainly for NEW USERS. You can see more complex recipes in the Cookbook. Customarily, we import as follows: In [1]: import numpy as np In [2]: import pandas as pd Object creation See the Data Structure Intro section. Creating a Series by passing a list of values, letting pandas create a default integer index: In [3]: s = pd.Series([1, 3, 5, np.nan, 6, 8]) In [4]: s Out[4]:  0    1.0 1    3.0 2    5.0 3    NaN 4    6.0 5    8.0 dtype: float64 Creating a DataFrame by passing a NumPy array, with a datetime index and labeled columns: In [5]: DATES = pd.date_range('20130101', periods=6) In [6]: dates Out[6]:  DatetimeIndex(['2013-01-01', '2013-01-02', '2013-01-03', '2013-01-04',               '2013-01-05', '2013-01-06'],              dtype='datetime64[ns]', freq='D') In [7]: df = pd.DataFrame(np.random.randn(6, 4), index=dates, columns=list('ABCD')) In [8]: df Out[8]:                     A         B         C         D 2013-01-01  0.469112 -0.282863 -1.509059 -1.135632 2013-01-02  1.212112 -0.173215  0.119209 -1.044236 2013-01-03 -0.861849 -2.104569 -0.494929  1.071804 2013-01-04  0.721555 -0.706771 -1.039575  0.271860 2013-01-05 -0.424972  0.567020  0.276232 -1.087401 2013-01-06 -0.673690  0.113648 -1.478427  0.524988