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.
|RangeIndex||Index implementing a monotonic integer range.|
|CategoricalIndex||Index based on an underlying Categorical.|
|MultiIndex||A multi-level, or hierarchical Index.|
|IntervalIndex||Immutable index of intervals that are closed on the same side.|
|DatetimeIndex||ndarray-like of datetime64 data.|
|TimedeltaIndex||ndarray of timedelta64 data, represented internally as int64|
|PeriodIndex||ndarray holding ordinal values indicating regular periods in time.|
|NumericIndex||Index of numpy int/uint/float data.|
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
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) # 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 !!