Indexing and Slicing Python Pandas DataFrame

Data Science, Programming

Different ways of indexing pandas dataframe

Indexing and Slicing Pandas Dataframe

Indexing and Slicing Pandas DataFrame can be done by their index position/index values.

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Refer to my story of Indexing vs Slicing in Python

Different ways of Indexing

1. Standard Indexing
2. loc
3. iloc

How to create DataFrame from csv_file.

Let’s see how to select rows and columns from the below-mentioned dataframe.

import pandas as pd
df=pd.read_csv("C:pandas_experimentpandas_indexing_slicingdata.csv")
df

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Standard Indexing

Standard indexing can be done by[] notation.

1. Selecting a single column

df[“Skill”]

If we select one column, it will return a series.

type(df["Skill"])
#Output:pandas.core.series.Series

2.Selecting multiple columns

To select multiple columns, we have to give a list of column names.

df[[“EmpID”,”Skill”]]

If we select multiple columns, it will return a dataframe.

type(df[["EmpID","Skill"]])
#Output:pandas.core.frame.DataFrame

3.Selecting rows using a slice object

df[0:2]

It will select row 0 and row 1. The end index is exclusive, the same as python slice.

4. Step is also mentioned in slice object

df[0:4:2]

It will start at row 0 and increment by step 2 and end at row4(exclusive).Same as python slice.

4. Selecting multiple rows and a single column

df[0:2][“EmpID”]

5. Selecting rows using a slice of row_index values

First, we will set the column “Name” as row_index

df1=df.set_index("Name")
df1

df1[“Indhu”:”Palani”]

If we mention a slice of row_index values, the end index is inclusive.

6.Selecting multiple rows and single column using row_index values

df1[“Indhu”:”Palani”][“Age”]

Note:

• We can select columns by specifying column_names only.
• We can select rows by mentioning the slice of row_index values /row_index position.
• While selecting rows, if we use a slice of row_index position, the end index is exclusive. But if we use a slice of row_index values/label, the end index is inclusive.
• If we select a single column or multiple rows with a single column, it will return a series.
• Selecting multiple rows and multiple columns will raise a KeyError

df1[“Indhu”:”Palani”][“Skill”,”Age”]

#Output:

KeyError: (‘Skill’, ‘Age’)

• We have to select rows by mentioning slice only. If we mention row_index or list of row_index, it will raise KeyError.

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iloc

.iloc is primarily integer position based (from 0 to length-1 of the axis), but may also be used with a boolean array.
 .iloc will raise IndexError if a requested indexer is out-of-bounds, except slice indexers which allow out-of-bounds indexing. –Python docs
Allowed inputs are:

• An integer e.g. 5.
• A list or array of integers [4, 3, 0]
• A slice object with ints 1:7.
• A boolean array (any NA values will be treated as False).
• A callable function with one argument (the calling Series or DataFrame) and that returns valid output for indexing (one of the above).

boolean array and callable function → will save this for future post.

Syntax

df.iloc[row_index_position,col_index_position]

1.Selecting a single row using iloc.

df.iloc[0]

If we select a single row alone, it will return a series.

type(df.iloc[0])
#Output:pandas.core.series.Series

2. Selecting multiple rows using iloc

If we have to select multiple rows, have to specify a list of row_index.

df.iloc[[0,1]]

If we select multiple rows, it will return a dataframe.

type(df.iloc[[0,1]])
#Output:pandas.core.frame.DataFrame

df.iloc[[0,3]]

3. Selecting multiple rows and multiple columns using iloc

df.iloc[[0,1],[0,1]]

Both row and column are mentioned as index positions only.

4. Selecting a single row and multiple columns using iloc

df.iloc[[0],[0,1]]

5.Selecting multiple rows and single column using iloc

df.iloc[[0,1],[0]]

6. Selecting rows by using the row_index position after setting the column as row_index.

df1.iloc[[0,2]]

The row_index position only mentioned.

7. Selecting rows by using slice object in iloc

df.iloc[::-1]

If the start and stop index not mentioned, by default it will start from row 0 and end at the last row.step -1 means in the reverse direction

8. Selecting row and columns using slice object in iloc

df1.iloc[0:4:2,0:2]

0:4:2 Row_index position. start at row 0, stop at row 4 and increment by 2 (step=2)

0:2 Column_index position . start at column 0, end at column 2.

If we use the index position in the slice object, the stop index is exclusive

9.IndexError

If we specify column index labels in iloc, it will raise IndexError

df1.iloc[[0,2],["EmpID"]]
#Output:IndexError: .iloc requires numeric indexers, got ['EmpID']

If we mention row_index values and column_index values,it will raise IndexError

df1.iloc[["Indhu","Palani"],["EmpID"]]
#Output:IndexError: .iloc requires numeric indexers, got ['Indhu' 'Palani']

Note

• By using iloc, we can’t select a single column alone or multiple columns alone.
• We have to mention the row_index position and column_index position only.
• If we mention row_index values or column_index values,it will raise IndexError.
• When we use slice object in iloc, the stop index is exclusive
• If we select a single row, it will return a series.

Return Type

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loc

loc is primarily label based, but may also be used with a boolean array. 
loc will raise KeyError when the items are not found. –Python docs

Allowed inputs are:

1. Single label ‘a’
2. List of labels [‘a’,’b’,’c’]
3. A slice object with labels [‘a’:’c’] . Both start and stop index are
4. A boolean array (any NA values will be treated as False).
5. A callable function with one argument (the calling Series or DataFrame) and that returns valid output for indexing (one of the above).

Boolean array and callable function → will save this for future post.

Syntax:

df.loc[row_index_label,col_index_label]

1.Selecting single row using loc

df.loc[0]

If we select a single row, it will return a series.

df1.loc[“Indhu”]

2.Selecting multiple rows using loc

To select multiple rows, we have to mention a list of labels.

df.loc[[0,1]]

df1.loc[[“Indhu”,”Karthi”]]

If we select multiple rows, it will return a dataframe.

3. Selecting single row and single column using loc

df.loc[[0],’EmpID’]

or

df.loc[0,’EmpID’]

Integers are valid labels, but they refer to the label, not the position. Here 0 refers to the label.

4.Selecting single row and multiple columns using loc

df.loc[[0],[‘EmpID’,’Skill’]]

or

df.loc[0,[‘EmpID’,’Skill’]]

If we mention row and column label as list means, it will return a dataframe

If we mention the row label as a single label and column label as list means, it will return a series.

df1.loc[[“Indhu”],[‘EmpID’,’Skill’]]

6. Selecting rows using a slice object in loc

df.loc[0:4:2]

start at row 0 and stop at row 4, increment by 2. If we use the index label in the slice object, the end index is inclusive

If we use loc ,it is purely label based indexing. Integers are valid labels, but they refer to the label, not the position. Here 0 refers to the label.

7. Selecting rows and columns using slice object in loc

df1.loc[::2,”EmpID”:”Age”]

::2 → Increment by step 2 from the first row to last row.

”EmpID”:”Age”-> It includes columns from “EmpID” to “Age”

Note

• By using loc, we can’t select a single column alone or multiple columns alone.
• We have to mention the row_index label and column_index label only.
• If we mention row_index position or column_index position,it will raise KeyError.
• If we select a single row, it will return a series.
• If we give a slice object as row_index /column_index, it should not be written within list[].

Return Type

Example:

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Conclusion:

• Using Standard indexing[], we can select a single column or multiple columns. But by using loc and iloc, we can’t select a single column alone or multiple columns alone.
• Using standard indexing[], we can select rows by using a slice object only. We can mention row_index values/positions in slice objects.If we use row_index values,end_index is inclusive.If we use the row_index position, the end index is exclusive
• Using loc, it’s purely label based indexing. When slicing is used in loc, both start and stop index is inclusive.
• Using iloc, it’s purely integer based indexing. These are 0-based indexing. When slicing is used in iloc, the start bound is included, while the upper bound is excluded.

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Github Link:

My jupyter notebook and csv file used in the code can be downloaded from my GitHub link.

Resources(Python documentation):

• Indexing and Slicing

My Blog

Indexing vs Slicing in Python

Sorting a Python Pandas DataFrame