Pandas

Check the 10 minutes to Pandas too.

import pandas as pd

Import the library, we assume this was done on this page

Series

Pandas Series documentation online. Pandas has all kind of methods similar to Numpy like main, std, min, max,...

s = pd.Series([])

Initialize a series

s[<key>] = <value>

Assign <value> to the series element with key <key>

When an element is initialized with a numeric key you can address it as s[<numkey>]. The order in the series is the order in which they are created, NOT the numeric order.

Elements initialized with a named key can be addressed as s[<key>], s.<key> or s[<numkey>]. Where <numkey> is defined by the order the element was created.

s.index

All indexes in the series

s.describe()

Series statistics

All in 1 example:

import numpy as np
import pandas as pd
s = pd.Series([])
for i in range(50):
    s[i] = int(np.random.random() * 100)

for i in s.index:
    print(i,s[i])

Funny, you can do s[0] but not

for i in s:
    print(s[i])

To get all values from the series you do:

for v in s:
    print(v)

To get the indexes too:

for i in s.index:
    print(i,s[i])

DataFrame

Object for tabular data (that is e.g. obtained by html_read).

table.head()

Return first 5 data rows of table.

table.columns=[list,of,column,names]

Redefine the column headers

table.<column>

Address a column by its name. Each column is a pandas Series

Other

read_html

Read html tables into a list of dataframes

Example code, most is selfexplaining I think. The fileurl can be local or remote, decimal specifies the decimal point character.

tables = pd.read_html(fileurl,header=0,index_col=0,decimal=<char>)