Pandas
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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>
ors[<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.
- table.head()
- Return first 5 data rows of table.
- table.columns=[list,of,column,names]
- Redefine the column headers
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>)