# 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
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
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` 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
# but  will assume it as explicit index,
# and gives us first item
pd_series
10

#### a. Using loc

`.loc()` always reference the explicit index scheme
pd_series.loc
10

#### b. Using iloc

`.iloc()` always reference the implicit index scheme
pd_series.iloc
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
Text
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
Text
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
Text
California
Texas
New York
Florida
Illinois
population
38332521
26448193
19651127
19552860
12882135
area
423967
695662
141297
170312
149995

#### a. Accessing row

states_dataframe.values
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']
Text
population
area
California
38332521
423967
Texas
26448193
695662
New York
19651127
141297
# selection on both rows and columns
states_dataframe.loc[:'New York',:'area']
Text
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
population 19651127
area 141297
Name: New York, dtype: int64
states_dataframe.iloc[:3]
Text
population
area
California
38332521
423967
Texas
26448193
695662
New York
19651127
141297
states_dataframe.iloc[:3,:1]
Text
population
California
38332521
Texas
26448193
New York
19651127