Indexing Pandas Series And Dataframe

Techniques learned in Numpy like indexing, slicing, fancy indexing, boolean masking and combination - will be applied to Pandas Series and DataFrame objects

1. DATA INDEXING & SELECTION ON SERIES

Series object acts in many ways like a one-dimensional NumPy array, and in many ways like a standard Python dictionary , we will see how.

1.1 Series as Dictionary

Series essentially maps a collection of keys to collection of values

import numpy as np
import pandas as pd 

# making Data Series
data_series = pd.Series([1,2,3,4,5],
                       index=['a','b','c','d','e'])
data_series

a    1
b    2
c    3
d    4
e    5
dtype: int64

• We can use dictionary like Python expressions

'a' in data_series

True

• We can fetch index of Series object using .keys() method

data_series.keys()

Index(['a', 'b', 'c', 'd', 'e'], dtype='object')

• We can fetch index,value pair using .items() method

list(data_series.items())

[('a', 1), ('b', 2), ('c', 3), ('d', 4), ('e', 5)]

• Just like Python Dictionary, we can append Panda Series with index and its value

data_series['f'] = 6
data_series

a    1
b    2
c    3
d    4
e    5
f    6
dtype: int64

1.2. Series as one-dimensional array

We can perform same operations on Series object as we do on Numpy Arrays — indexing, slicing, masking, fancy indexing

• Indexing by providing explicit index (string, in our case)

data_series['d']

4

• Slicing with string as index ALERT: Notice that when you are slicing with an explicit index (i.e., data[:'d']), the stop index is included in the slice

data_series[:'d']

a    1
b    2
c    3
d    4
dtype: int64

• Indexing by providing implicit (integer) index

data_series[0]

1

• Slicing by providing implicit (integer) index. ALERT , note that stop index isn’t included in the output

data_series[1:3]

b    2
c    3
dtype: int64

1.3. Masking & Fancy Indexing

• In masking, we provide the boolean array under [] to get subset of Series This boolean array can be the result of some conditional operator. For masking, we can pass single condition or group of conditions. We will examine all this concepts in the examples below:

# conditional operator that result in boolean array
data_series > 3 

a    False
b    False
c    False
d     True
e     True
f     True
dtype: bool

# boolean masking
data_series[(data_series > 3)]

d    4
e    5
f    6
dtype: int64

# another masking example with multiple conditions 
data_series[(data_series > 0) & (data_series <4)]

a    1
b    2
c    3
dtype: int64

• Fancy Indexing is where we need to fetch values at arbitrary index points, as compared to simple slicing where we fetch values in some order ([1:10], [::2], for example)

# fetch first and last item of the Series
data_series[[0,-1]]

a    1
f    6
dtype: int64

# fetch index values of 'a' and 'e' indices
data_series[['a','e']]

a    1
e    5
dtype: int64

1.4. Indexers: loc, iloc

PROBLEM:

• We have seen above in the example of slicing that how explicit indexing makes things confusing, this is specially true if the indices are in integer.

• For example, if your Series has an explicit integer index, an indexing operation such as data[1] will use the explicit indexing, that is fetch the value of index labeled 1 and not the second item as in the implicit indexing. However, slicing operation like data[1:3] will use the implicit Python-style slicing, that is, fetching 2nd and 3rd items in the Series object

SOLUTION:

• Because of this potential confusion in the case of integer indexes, Pandas provides some special indexer attributes that explicitly expose certain indexing schemes:

# first make pd.Series where confusion can happen
pd_series = pd.Series([10,20,30,40,50],
                     index=[1,2,3,4,5])
pd_series

1    10
2    20
3    30
4    40
5    50
dtype: int64

# Now let suppose you want to get the value of second index[1]
# but [1] will assume it as explicit index, 
# and gives us first item
pd_series[1]

10

a. Using loc

.loc() always reference the explicit index scheme

pd_series.loc[1]

10

b. Using iloc

.iloc() always reference the implicit index scheme

pd_series.iloc[1]

20

2. DATA INDEXING & SELECTION IN A DATAFRAME

DataFrame object acts in many ways like a two-dimensional NumPy array, and in many ways like a dictionary of related Series objects, we will see how:

2.1. DataFrame as a Dictionary

DataFrame as a dictionary of related Series objects

# reproducing the data series we constructed earlier
# reproducing population dictionary
population_dict = {'California': 38332521, 
                   'Texas': 26448193, 
                   'New York': 19651127, 
                   'Florida': 19552860, 
                   'Illinois': 12882135}
population_series = pd.Series(population_dict)

# making the area dictionary 
area_dict = {'California': 423967, 
             'Texas': 695662, 
             'New York': 141297, 
             'Florida': 170312, 
             'Illinois': 149995}
area_series = pd.Series(area_dict)

states_dataframe = pd.DataFrame({'population': population_series,
                                'area': area_series})
states_dataframe

	population	area
California	38332521	423967
Texas	26448193	695662
New York	19651127	141297
Florida	19552860	170312
Illinois	12882135	149995

• Individual column data can be accesses via dictionary style indexing

states_dataframe['population']

California    38332521
Texas         26448193
New York      19651127
Florida       19552860
Illinois      12882135
Name: population, dtype: int64

• We can also access the column values through the column name as attribute

states_dataframe.population

California    38332521
Texas         26448193
New York      19651127
Florida       19552860
Illinois      12882135
Name: population, dtype: int64

• Dictionary-style syntax can be used to modify the object or add new column to DataFrame object

states_dataframe['density'] = states_dataframe['population'] / states_dataframe['area']
states_dataframe

	population	area	density
California	38332521	423967	90.413926
Texas	26448193	695662	38.018740
New York	19651127	141297	139.076746
Florida	19552860	170312	114.806121
Illinois	12882135	149995	85.883763

2.2. DataFrame as two-dimensional Array

• .values method provides underlying values of DataFrame object

states_dataframe.values

array([[38332521,   423967],
       [26448193,   695662],
       [19651127,   141297],
       [19552860,   170312],
       [12882135,   149995]])

• .T method transposes (columns to rows, rows to columns) the DataFrame object

states_dataframe.T

	California	Texas	New York	Florida	Illinois
population	38332521	26448193	19651127	19552860	12882135
area	423967	695662	141297	170312	149995

a. Accessing row

states_dataframe.values[0]

array([38332521,   423967])

b. Accessing column

💡 Remember that [] indexing applies to column labels in DataFrame object as opposed to row labels in Series object

states_dataframe['population']

California    38332521
Texas         26448193
New York      19651127
Florida       19552860
Illinois      12882135
Name: population, dtype: int64

2.3. Using Indexers: loc, iloc

a. Using loc

.loc() always reference the explicit index scheme

states_dataframe.loc['New York']

population    19651127
area            141297
Name: New York, dtype: int64

states_dataframe.loc[:'New York']

	population	area
California	38332521	423967
Texas	26448193	695662
New York	19651127	141297

# selection on both rows and columns
states_dataframe.loc[:'New York',:'area']

	population	area
California	38332521	423967
Texas	26448193	695662
New York	19651127	141297

b. Using iloc

.iloc() always reference the implicit index scheme

states_dataframe.iloc[2]

population    19651127
area            141297
Name: New York, dtype: int64

states_dataframe.iloc[:3]

	population	area
California	38332521	423967
Texas	26448193	695662
New York	19651127	141297

states_dataframe.iloc[:3,:1]

	population
California	38332521
Texas	26448193
New York	19651127