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 online documentation.
A pandas series is a 1 dimensional array with named keys.
Pandas Series have all kind of methods similar to Numpy like main, std, min, max,.... In fact Pandas is using numpy to do this.

s = pd.Series([])

s = pd.Series([valuelist],[indexlist])

Initialize a series. If indexlist is omitted the keys are integers starting at 0.

s[<key>] = <value>

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

The order in the series is the order in which they are created, NOT the numeric order.

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

Once you have used named keys in a series you cannot create new elements with a numeric key.

s.index

All indexes in the series. Can be sliced to find a particular index.

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 read_html).

table.head(x)

table.tail(x)

Return first/last x data rows of table (5 is the default value for x).

table.columns

The column headers (class = pandas.core.indexes.base.Index)

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

Redefine the column headers

table.index

The table index (first column) (class = pandas.core.indexes.base.Index)

table.<columname>

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

table.loc[<indexname>]

table.loc[<indexname>].<columnname>

table.loc[0][0]

table.loc[lambda d

d[colum1] == <value> ]

The content of the index (row) as pandas Series or just the named column. [0][0]-form for tables without header or index.

The last form selects all rows where column1 equals <value>

table.filter(regex=<regex>,axis='index')

table.filter(regex=<regex>,axis='index').<columnname>

table.filter(regex=<regex>,axis='index').index

Find all rows for which in index matches <regexp> or get only the column of the matched indexes. (axis=0 ) or the indexname(s)

table.filter(regex=<regex>,axis='columns')

Find all column-names which name matches <regexp>. (axis=1)

table.sort_values(<columnname>)

table.sort_values([<column1>,<colunm2>],ascending=(True,False))

Sort table on the values in the columns. The second form sorts on column1 first and then on column2, column1 ascending, column2 descending

table.groupby([column1,column2])

Group the table by the column-values

table.agg(newname=('columname', np.max))

This sample uses named aggregations, that is only supported from version v0.25

table.reset_index()

table.transform()

table.merge(table2,on='column')

Like SQL-join

table.assign

Add a column

table.drop(columns=[listofcolumnstodrop]

Remove columns from a dataframe.

Other

read_html(url)

Read html tables into a list of dataframes (no header, no index)

Example code. The first line in the table is a header, the first column the index (e.g. dates), decimal specifies the decimal point character.

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