Basic Functionality of Series or DataFrame in Pandas

Throughout this post I will take you over the fundamental mechanics of interacting with the data contained in a Series or DataFrame in pandas(python).

Reindexing

Reindexing is a critical method on pandas objects. 'Reindexing' means to create a new object with the data conformed to a new index. Here is my object that I will be using for this post

obj = pd.Series([4.1, 2.6, 1.1, 3.7], index=['d', 'b', 'a', 'c'])

By Calling reindex on this Series, it rearranges the data according to the new index, introducing missing values if any index values were not already present in last series.

obj2 = obj.reindex(['a', 'b', 'c', 'd', 'e'])

you can fill the missing value by passing fill_value as below

obj.reindex(['a', 'b', 'c', 'd', 'e'], fill_value=0)

You can fill by forward ('ffill') value or backward 'bfill'

obj3.reindex(range(6), method='ffill')

In DataFrame, reindex can alter either the (row) index, columns, or both.
When you passed just a sequence, the rows are reindexed in the result.

frame = pd.DataFrame(np.arange(27.0,31.5,0.5)).reshape((3, 3)), index=['a', 'c', 'd'], columns=['Colombo', 'Negombo', 'Gampaha'])

reindex
frame2 = frame.reindex(['a', 'b', 'c', 'd'])

reindex in rows in dataframe
cities= ['Colombo', 'Negombo', 'Kandy']
frame.reindex(columns=cities)

frame.reindex(index=['a', 'b', 'c', 'd'], method='ffill', columns=cities)

Dropping entries

In series, We can drop one or more entries from an axis
new_obj = obj.drop('c')

new_obj2 = obj.drop(['d', 'c'])

With DataFrame, index values can be deleted from either axis:
data.drop('a')
data.drop(['a','c'])

deleting entries over the axis

data.drop('Negombo', axis=1)

data.drop(['Negombo', 'Kandy'], axis=1)