Pandas Index Explained with Examples

  • Post author:
  • Post category:Pandas / Python
  • Post last modified:January 24, 2022

Pandas Index is an immutable sequence used for indexing DataFrame and Series. pandas.Index is a basic object that stores axis labels for all pandas objects.

DataFrame is a two-dimensional data structure, immutable, heterogeneous tabular data structure with labeled axis rows, and columns. pandas Dataframe is consists of three components principal, data, rows, and columns. In DataFrame the row labels are called index.

Series is a one-dimensional array that is capable of storing various data types (integer, string, float, python objects, etc.). We can easily convert the list, tuple, and dictionary into Series using the series() method. In Series, the row labels are called the index. The Series can have only one column, but it cannot contain multiple columns. List, NumPy Array, Dict can be turned into a pandas Series. 

1. What is pandas Index?

pandas have several classes to define the Index and an instance of Index can only contain hashable objects.

pandas IndexDescription
RangeIndexIndex implementing a monotonic integer range.
CategoricalIndexIndex based on an underlying Categorical.
MultiIndexA multi-level, or hierarchical Index.
IntervalIndexImmutable index of intervals that are closed on the same side.
DatetimeIndexndarray-like of datetime64 data.
TimedeltaIndexndarray of timedelta64 data, represented internally as int64
PeriodIndexndarray holding ordinal values indicating regular periods in time.
NumericIndexIndex of numpy int/uint/float data.
pandas Index classes

2. Create Index

You can create a pandas Index through its constructor. You can use any class from the above table to create an Index.


# Syntax of Index() constructor.
class pandas.Index(data=None, dtype=None, copy=False, name=None, tupleize_cols=True, **kwargs)
  • data – list of data you preffered to have on Index.
  • dtype – NumPy suppoted data type. When it is None, it uses best type s per the data.
  • copy – bool type. Make a copy of input ndarray
  • name – Name of the Index.
  • tupleize_cols – When True, attempt to create a MultiIndex if possible
  • **kwargs – **kwargs

3. Create Series with Index

Series can be created through its constructor


s=pd.Series(['A','B','C','D','E'])
print(s)

# Outputs
#0    A
#1    B
#2    C
#3    D
#4    E

This creates a Series with a default numerical index starting from zero. You can set the Index with the custom values while creating a Series object.


idx= ['idx1','idx2','idx3','idx4','idx5']
s=pd.Series(['A','B','C','D','E'],index=idx)
print(s)

# Outputs
#dtype: object
#idx1    A
#idx2    B
#idx3    C
#idx4    D
#idx5    E
#dtype: object

Now let’s create an Index from the RangeIndex() class. The below example creates Index starting from integer number 5.


idx=pd.RangeIndex(5,10)
s=pd.Series(['A','B','C','D','E'],index=idx)
print(s)

#Outputs
#5    A
#6    B
#7    C
#8    D
#9    E
#dtype: object

4. Create DataFrame with Index

One of the easiest ways to create a pandas DataFrame is by using its constructor.


# Create pandas DataFrame from List
import pandas as pd
technologies = [ ["Spark",20000, "30days"], 
                 ["pandas",20000, "40days"], 
               ]
df=pd.DataFrame(technologies)
print(df)

Since we are not giving labels to columns and rows(index), DataFrame by default assigns incremental sequence numbers as labels to both rows and columns called Index.


        0      1       2
0   Spark  20000  30days
1  pandas  20000  40days

Column names with sequence numbers don’t make sense as it’s hard to identify what data holds on each column hence, it is always best practice to provide column names that identify the data it holds. Use column param and index param to provide column & row labels respectively to the DataFrame.


# Add Column & Row Labels to the DataFrame
column_names=["Courses","Fee","Duration"]
row_label=["a","b"]
df=pd.DataFrame(technologies,columns=column_names,index=row_label)
print(df)

Yields below output.


  Courses    Fee Duration
a   Spark  20000   30days
b  pandas  20000   40days

5. Get DataFrame Index as List

Sometimes you may required to get the pandas DataFrame index as list of values, you can do this by using df.index.values. Note that df.index returns a Series object.


# Get Index as Series
print(df.index)
# Outputs
# RangeIndex(start=0, stop=3, step=1)

# Get Index as List
print(df.index.values)
# Outputs
# [0 1 2]

6. Set Labels to Index

The labels for the Index can be changed as shown in below.


# Set new Index
df.index = pd.Index(['idx1','idx2','idx3'])
print(df.index)

# Outputs
# Index(['idx1', 'idx2', 'idx3'], dtype='object')

7. Get Rows by Index

By using DataFrame.iloc[] property you can get the row by Index.


# Get Row by Index.
print(df.iloc[2])

# Outputs
Courses     Hadoop
Fee          26000
Duration    35days
Discount      1500
Name: idx3, dtype: object

8. Set Index to Column & Column to Index

DataFrame.reset_index() is used to set the Index as column and resets the Index from zero. The below example adds column with name as Index to DataFrame.


# Set Index to Column
df2=df.reset_index()
print(df2)

# Outputs
  index  Courses    Fee Duration  Discount
0  idx1    Spark  20000    30day      1000
1  idx2  PySpark  25000   40days      2300
2  idx3   Hadoop  26000   35days      1500

DataFrame.set_index() is used to set the DataFrame column as Index. The below example set’s Courses column as index.


# Set Column as Index
df2=df.set_index('Courses')
print(df2)

Happy Learning !!

NNK

SparkByExamples.com is a Big Data and Spark examples community page, all examples are simple and easy to understand and well tested in our development environment Read more ..

Leave a Reply

You are currently viewing Pandas Index Explained with Examples