How can I select a specific column from each row in a Pandas DataFrame?

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I have a DataFrame in this format:

    a   b   c
0   1   2   3
1   4   5   6
2   7   8   9
3   10  11  12
4   13  14  15

and an array like this, with column names:

['a', 'a', 'b', 'c', 'b']

and I’m hoping to extract an array of data, one value from each row. The array of column names specifies which column I want from each row. Here, the result would be:

[1, 4, 8, 12, 14]

Is this possible as a single command with Pandas, or do I need to iterate? I tried using indexing

i = pd.Index(['a', 'a', 'b', 'c', 'b'])

but I got a segfault, which I couldn’t diagnose because the documentation is lacking.

You could use lookup, e.g.

>>> i = pd.Series(['a', 'a', 'b', 'c', 'b'])
>>> df.lookup(i.index, i.values)
array([ 1,  4,  8, 12, 14])

where i.index could be different from range(len(i)) if you wanted.

How to select rows and columns in Pandas using [ ], .loc, iloc, .at and , . This method df[['a','b']] produces a copy. You can also use '. To select a single column, use square brackets [] with the column name of the column of interest. Each column in a DataFrame is a Series. As a single column is selected, the returned object is a pandas DataFrame. We can verify this by checking the type of the output:

For large datasets, you can use indexing on the base numpy data, if you're prepared to transform your column names into a numerical index (simple in this case):


out: array([ 1,  4,  8, 12, 14])

This will be much more efficient that list comprehensions, or other explicit iterations.

Selecting multiple columns in a pandas dataframe, Selecting disjointed rows and columns​​ loc . To select a single value from the DataFrame, you can do the following. You can use slicing to select a particular column. To select rows and columns simultaneously, you need to understand the use of comma in the square brackets. In Order to select a column in Pandas DataFrame, we can either access the columns by calling them by their columns name. Output: Column Addition: In Order to add a column in Pandas DataFrame, we can declare a new list as a column and add to a existing Dataframe.

You can always use list comprehension:

[df.loc[idx, col] for idx, col in enumerate(['a', 'a', 'b', 'c', 'b'])]

Selecting Subsets of Data in Pandas: Part 1 - Dunder Data, Subset selection is simply selecting particular rows and columns of data from a DataFrame (or Series). This could mean selecting all the rows and some of the  For example, if we have Pandas dataframe with multiple data types, like numeric and object and we will learn how to select columns that are numeric. We can use Pandas’ seclect_dtypes() function and specify which data type to include or exclude. This will allow us to select/ ignore columns by their data types.

Using iloc, loc, & ix to select rows and columns in Pandas DataFrames, iloc returns a Pandas Series when one row is selected, and a Pandas DataFrame when multiple rows are selected, or if any column in full is selected. To counter  Select multiple row & columns by Labels in DataFrame using loc[] To select multiple rows & column, pass lists containing index labels and column names i.e. It will return a subset DataFrame with given rows and columns i.e. Only Rows with index label ‘b’ & ‘c’ and Columns with names ‘Age’, ‘Name’ are in returned DataFrame object.

Indexing and selecting data, A callable function with one argument (the calling Series or DataFrame) and that returns You can pass a list of columns to [] to select columns in that order. In this article we will discuss different ways to select rows in DataFrame based on condition on single or multiple columns. Following Items will be discussed, Select Rows based on value in column. Select Rows based on any of the multiple values in column. Select Rows based on any of the multiple conditions on column.

How do I select a subset of a DataFrame?, How do I select specific columns from a DataFrame ?¶ ../. Each column in a DataFrame is a Series . As a single column is selected, the returned object is a  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. You can imagine that each row has a row number from 0 to the total rows (data.shape[0]) and iloc[] allows selections based on these numbers.

  • That’s fantastic, thank you! Is it also possible to assign to those indexes?
  • You can assign, but ONLY when the frame is a single dtype (as it is now). df.unstack().loc[zip(i.values,i.index)] = [1,2,3,4,5]. And you must match the length on both sides (you can also select using this syntax); see this issue:
  • If you want to add the index, make a series: pd.Series(df.lookup(i.index, i.values), index=i.index)