import numpy as np
import pandas as pd

dframe = pd.DataFrame(np.random.randn(8,4), columns=['A','B','C','D'])
dframe

dframe = pd.DataFrame(np.random.randn(8,4), columns=['A','B','C','D'], index = list('abcdefgh'))
dframe

dfdict = pd.DataFrame({'A':[1,2,3,4],'B': [5,6,7,8]},index = ['one','two','three','four'])
dfdict

dframe[0:4]

dframe.iloc[0:4,0:2]
The .loc method provides a way to use the labels of the dataframe effectively. It accepts integers as arguments but these are strictly related to the labels, not the position of a row/column. 
dframe.loc[list('bca'),['A','B']]

dframe[dframe['C']>0]

dframe[(dframe['C']>0) & (dframe['D']<0)]

dframe['A'] = dframe['A'].map(lambda x: x+1)
dframe

gb = dframe.groupby(dframe['A']>0)
gb.size()

gb.sum()

gb.median()

gb.groups

def square(x):
    return pd.Series([x, x**2])

dframe['A'].apply(square)