"""Given a dataframe df to filter by a series s:""" 
df[df['col_name'].isin(s)]

"""to do the same filter on the index instead of arbitrary column"""
df.ix[s]

""" display only certain columns, note it is a list inside the parans """
df[['A', 'B']]

"""drop rows with atleast one null value, pass params to modify 
to atmost instead of atleast etc.""" 
df.dropna()

"""deleting a column""" 
del df['column-name'] # note that df.column-name won't work. 

"""filter by multiple conditions in a dataframe df
   parentheses!""" 
df[(df['gender'] == 'M') & (df['cc_iso'] == 'US')]

"""filter by conditions and the condition on row labels(index)"""
df[(df.a > 0) & (df.index.isin([0, 2, 4]))]

"""quick way to create a data frame to try things out""" 
df = pandas.DataFrame(np.random.randn(5, 4), columns=['a', 'b', 'c'])

"""creating complex filters using functions on rows: http://goo.gl/r57b1"""
df[df.apply(lambda x: x['b'] > x['c'], axis=1)]

"""Pandas replace operation http://goo.gl/DJphs"""
df[2].replace(4, 17, inplace=True)
df[1][df[1] == 4] = 19

"""apply and map examples"""
"""add 1 to every element"""
df.applymap(lambda x: x+1)

"""add 2 to row 3 and return the series"""
df.apply(lambda x: x[3]+2,axis=0)

"""add 3 to col A and return the series"""
df.apply(lambda x: x['a']+1,axis=1)

"""example of applying a complex external function 
to each row of a data frame""" 
def stripper(x):
    l = re.findall(r'[0-9]+(?:\.[0-9]+){3}', x['Text with IP adress embedded'])
    # you can take care of special 
    # cases and missing values, more than expected 
    # number of return values etc like this. 
    if l == []:
        return ''
    else: 
        return l[0]

df.apply(stripper, axis=1)

"""groupby used like a histogram to obtain counts on sub-ranges of a variable, pretty handy""" 
df.groupby(pd.cut(df.age, range(0, 130, 10))).size()

"""finding the distribution based on quantiles""" 
df.groupby(pd.qcut(df.age, [0, 0.99, 1])

"""if you don't need specific bins like above, and just want to count number of each values"""
df.age.value_counts()

"""one liner to normalize a data frame""" 
(df - df.mean()) / (df.max() - df.min())

"""grouping and applying a group specific function to each group element, 
I think this could be simpler, but here is my current version""" 
quantile = [0, 0.50, 0.75, 0.90, 0.95, 0.99, 1]
grouped = df.groupby(pd.qcut(df.age, quantile))
frame_list = []
for i, group in enumerate(grouped):
   (label, frame) = group
   frame['age_quantile'] = quantile[i + 1]
   frame_list.append(frame)
self.df = pd.concat(frame_list)