Difference between revisions of "Pandas"
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Line 7: | Line 7: | ||
;s = pd.Series([]) | ;s = pd.Series([]) | ||
:Initialize a 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 <code>s[<numkey>]</code>. 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 <code>s[<key>]</code>, <code>s.<key></code> or <code>s[<numkey>]</code>. Where <numkey> is defined by the order the element was created. | ||
;s.index | ;s.index | ||
:All indexes in the series | :All indexes in the series |
Revision as of 23:07, 16 December 2018
Check the 10 minutes to Pandas too.
- import pandas as pd
- Import the library, we assume this was done on this page
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])