# Part 2: 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

Article Contents

## 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
``````
• 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
``````

### 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

``````

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

#### 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]
``````
``````states_dataframe.iloc[:3,:1]
``````