echoing @HYRY, see the new docs in 0.11
http://pandas.pydata.org/pandas-docs/stable/indexing.html
Here we have new operators, .iloc to explicity support only integer indexing, and .loc to explicity support only label indexing
e.g. imagine this scenario
In [1]: df = pd.DataFrame(np.random.rand(5,2),index=range(0,10,2),columns=list('AB'))
In [2]: df
Out[2]:
A B
0 1.068932 -0.794307
2 -0.470056 1.192211
4 -0.284561 0.756029
6 1.037563 -0.267820
8 -0.538478 -0.800654
In [5]: df.iloc[[2]]
Out[5]:
A B
4 -0.284561 0.756029
In [6]: df.loc[[2]]
Out[6]:
A B
2 -0.470056 1.192211
[] slices the rows (by label location) only
Top answer 1 of 8
815
echoing @HYRY, see the new docs in 0.11
http://pandas.pydata.org/pandas-docs/stable/indexing.html
Here we have new operators, .iloc to explicity support only integer indexing, and .loc to explicity support only label indexing
e.g. imagine this scenario
In [1]: df = pd.DataFrame(np.random.rand(5,2),index=range(0,10,2),columns=list('AB'))
In [2]: df
Out[2]:
A B
0 1.068932 -0.794307
2 -0.470056 1.192211
4 -0.284561 0.756029
6 1.037563 -0.267820
8 -0.538478 -0.800654
In [5]: df.iloc[[2]]
Out[5]:
A B
4 -0.284561 0.756029
In [6]: df.loc[[2]]
Out[6]:
A B
2 -0.470056 1.192211
[] slices the rows (by label location) only
2 of 8
124
The primary purpose of the DataFrame indexing operator, [] is to select columns.
When the indexing operator is passed a string or integer, it attempts to find a column with that particular name and return it as a Series.
So, in the question above: df[2] searches for a column name matching the integer value 2. This column does not exist and a KeyError is raised.
The DataFrame indexing operator completely changes behavior to select rows when slice notation is used
Strangely, when given a slice, the DataFrame indexing operator selects rows and can do so by integer location or by index label.
df[2:3]
This will slice beginning from the row with integer location 2 up to 3, exclusive of the last element. So, just a single row. The following selects rows beginning at integer location 6 up to but not including 20 by every third row.
df[6:20:3]
You can also use slices consisting of string labels if your DataFrame index has strings in it. For more details, see this solution on .iloc vs .loc.
I almost never use this slice notation with the indexing operator as its not explicit and hardly ever used. When slicing by rows, stick with .loc/.iloc.
Pandas
pandas.pydata.org › pandas-docs › stable › user_guide › indexing.html
Indexing and selecting data — pandas 3.0.5 documentation
Trying to use a non-integer, even a valid label will raise an IndexError. The .iloc attribute is the primary access method. The following are valid inputs: An integer e.g. 5. A list or array of integers [4, 3, 0]. A slice object with ints 1:7. A boolean array. A callable, see Selection By Callable. A tuple of row (and column) indexes, whose elements are one of the above types.
python - Select Pandas rows based on list index - Stack Overflow
If callable, the callable function ... if the row should be skipped and False otherwise. An example of a valid callable argument would be lambda x: x in [0, 2] This feature works in version pandas 0.20.0+. See also the corresponding issue and a related post. ... Save this answer. ... Show activity on this post. What you are trying to do is to filter your dataframe by index... More on stackoverflow.com
How to extract rows from a Pandas dataframe using .loc[]? - Python - Data Science Dojo Discussions
The Pandas loc[] method is used to select rows from a Pandas dataframe based on their index labels. It takes a list of index labels as input and returns a new dataframe containing only the rows with those labels. This method is useful for extracting specific rows from a dataframe or for subsetting ... More on discuss.datasciencedojo.com
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November 24, 2022How to select a row by index and replace a value.
return it back to the original df It would be a lot easier to just make a brand new df, and then replace the old one with the new one. keys = df['Item'].unique() values = ["_".join(df['Model #s'][df["Item"]==key]) for key in keys] new_df = pd.DataFrame(zip(keys, values), columns=['Item','Model #s']) df = new_df # replace the old df with the new one More on reddit.com
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December 27, 2023How to group rows in a dataframe by consecutive values in sequence
df[“event”] = ((df.frame - df.frame.shift() - 1) != 0).cumsum() More on reddit.com
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March 12, 2022Statology
statology.org › home › how to select rows by index in a pandas dataframe
How to Select Rows by Index in a Pandas DataFrame
March 27, 2025 - Another useful way to select rows is by using boolean conditions on the index. This method gives you flexibility to filter rows based on specific conditions applied to the index values. Let’s look at how to select rows where the index is greater than a certain value: import pandas as pd import numpy as np #make this example reproducible np.random.seed(0) #create DataFrame df = pd.DataFrame(np.random.rand(6,2), index=range(0,18,3), columns=['A', 'B']) #view DataFrame df A B 0 0.548814 0.715189 3 0.602763 0.544883 6 0.423655 0.645894 9 0.437587 0.891773 12 0.963663 0.383442 15 0.791725 0.528895 #select rows where index is greater than 6 df[df.index > 6] A B 9 0.437587 0.891773 12 0.963663 0.383442 15 0.791725 0.528895
Note.nkmk.me
note.nkmk.me › home › python › pandas
pandas: Select rows/columns by index (numbers and names) | note.nkmk.me
August 8, 2023 - s_bool_wrong = pd.Series([True, False, False, True, True], index=['A', 'B', 'C', 'D', 'E']) # print(df[s_bool_wrong]) # IndexingError: Unalignable boolean Series provided as indexer (index of the boolean Series and of the indexed object do not match). ... Using a Boolean Series, you can select rows by conditions.
Spark By {Examples}
sparkbyexamples.com › home › pandas › pandas select rows by index (position/label)
Pandas Select Rows by Index (Position/Label) - Spark By {Examples}
November 14, 2024 - Use Pandas DataFrame.iloc[] & DataFrame.loc[] to select rows by integer Index and by row indices respectively. iloc[] attribute can accept single
Spark By {Examples}
sparkbyexamples.com › home › pandas › pandas select rows based on list index
Pandas Select Rows Based on List Index - Spark By {Examples}
October 28, 2024 - You can select rows in a Pandas DataFrame based on a list of indices, you can use the DataFrame.iloc[], DataFrame.loc] methods. iloc[] takes row indexes
GeeksforGeeks
geeksforgeeks.org › pandas › select-rows-columns-by-name-or-index-in-pandas-dataframe-using-loc-iloc
How to Select Rows & Columns by Name or Index in Pandas Dataframe - Using loc and iloc - GeeksforGeeks
November 28, 2024 - The .iloc[] method selects data based on integer positions (index numbers). It is particularly useful when you don’t know the labels but know the positions. ... Uses integer positions (0, 1, 2, ...) to index rows and columns.
GeeksforGeeks
geeksforgeeks.org › pandas › indexing-and-selecting-data-with-pandas
Indexing and Selecting Data with Pandas - GeeksforGeeks
The .iloc[] function is used for position-based indexing. It allows us to access rows and columns by their integer positions. It is similar to .loc[] but only accepts integer-based indices to specify rows and columns.
Published: April 28, 2026
Codegive
codegive.com › blog › pandas_choose_row_by_index.php
Mastering Pandas Choose Row by Index: Unlock Precision Data Selection & Supercharge Your Analysis!
To select a row by index in pandas, use .loc[] for label-based indexing (where the index values themselves are labels) or .iloc[] for integer-position based indexing (where you select by the row's numerical position).
Bobby Hadz
bobbyhadz.com › blog › pandas-select-rows-by-index-list
Pandas: Select rows based on a List of Indices | bobbyhadz
April 12, 2024 - Use the DataFrame.iloc position-based indexer to select rows in a DataFrame based on a list of indices. The iloc property will return a new DataFrame containing only the rows at the specified indices.
Medium
medium.com › @amit25173 › how-to-use-pandas-get-row-by-index-b01fa9339cdf
How to Use pandas Get Row by Index? | by Amit Yadav | Medium
April 12, 2025 - ... To get a specific row by its index, you can use the .loc or .iloc methods. The main difference is that .loc accesses rows based on label-based indexing, while .iloc is entirely integer-location-based.
Python Examples
pythonexamples.org › pandas-dataframe-select-rows-based-on-index-condition
Pandas DataFrame - Select rows based on Index condition
We shall use the condition that the index should be exactly divisible by 2. Follow the same steps as in the previous example, except for the condition in Step 3. The condition to select even indexed rows is as shown below. ... The complete program to select rows from a DataFrame whose index value is an even number is even. import pandas as pd # Take a DataFrame df = pd.DataFrame({ 'name': ['apple', 'banana', 'cherry', 'fig', 'mango'], 'quantity': [14, 0, 0, 37, 25], 'price': [100, 50, 20, 30, 150] }) # Select rows whose index is even df_selected_rows = df[df.index % 2 == 0] # Print DataFrame print(f"Original DataFrame\n{df}\n") print(f"Selected Rows\n{df_selected_rows}")
YouTube
youtube.com › watch
Select Rows of pandas DataFrame by Index in Python (2 Examples) | Extract & Get Row | Multiple Lines - YouTube
How to extract pandas DataFrame rows by index positions in the Python programming language. More details: https://statisticsglobe.com/select-rows-of-pandas-d...
Published: January 18, 2023
Machine Learning Plus
machinelearningplus.com › blog › pandas iloc – how to select rows using index in dataframes?
Pandas iloc - How to select rows using index in DataFrames? - machinelearningplus
March 8, 2022 - You can pass a single integer value as the row index to select a single row across all the columns from the dataframe. ... By specifying both the row and column indices to the iloc function, you can also view a specific data point.
Saturn Cloud
saturncloud.io › blog › how-to-search-pandas-data-frame-by-index-value-and-value-in-any-column
How to Search Pandas Data Frame by Index Value and Value in Any Column | Saturn Cloud Blog
May 1, 2026 - To search a pandas data frame by index value, you can use the .loc[] method. The .loc[] method allows you to select rows and columns by label, and it can accept a variety of input formats.
Top answer 1 of 8
253
Use .iloc for integer based indexing and .loc for label based indexing. See below example:
ind_list = [1, 3]
df.iloc[ind_list]
2 of 8
155
you can also use iloc:
df.iloc[[1,3],:]
This will not work if the indexes in your dataframe do not correspond to the order of the rows due to prior computations. In that case use:
df.index.isin([1,3])
... as suggested in other responses.
Statology
statology.org › home › how to select columns by index in a pandas dataframe
How to Select Columns by Index in a Pandas DataFrame
March 27, 2025 - If you’d like to select columns based on label indexing, you can use the .loc function. This tutorial provides an example of how to use each of these functions in practice. The following code shows how to create a pandas DataFrame and use .iloc to select the column with an index integer value of 3:
Shane Lynn
shanelynn.ie › home › pandas iloc and loc – quickly select rows and columns in dataframes
Pandas iloc and loc – quickly select data in DataFrames
October 16, 2021 - The iloc indexer syntax is data.iloc[<row selection>, <column selection>], which is sure to be a source of confusion for R users. “iloc” in pandas is used to select rows and columns by number, in the order that they appear in the data frame.
IncludeHelp
includehelp.com › python › select-pandas-rows-based-on-list-index.aspx
Select Pandas rows based on list index
When it comes to selecting a row or column value, we always use pandas.DataFrame.loc property or pandas.DataFrame.iloc property. To select pandas rows based on the list index, we will select an index of those rows with certain sequence numbers which indicate a list. If we want row 1 and row ...
Data Science Dojo
discuss.datasciencedojo.com › python
How to extract rows from a Pandas dataframe using .loc[]? - Python - Data Science Dojo Discussions
November 24, 2022 - The Pandas loc[] method is used to select rows from a Pandas dataframe based on their index labels. It takes a list of index labels as input and returns a new dataframe containing only the rows with those labels.