## Pandas DataFrame - Selecting and Indexing

pandas set index

pandas dataframe filter by column value

pandas select columns by condition

pandas iloc

pandas dataframe index column

pandas loc

pandas get column name by index

I have this dataframe pandas object

df = pd.DataFrame(randn(5,4),['A','B','C','D','E'],['W','X','Y','Z'])

I execute and this is the table of rows `A, B, C, D, E`

and `W, X, Y, Z`

columns

Each one of these `W, X, Y, Z`

columns are really a Pandas Series, `W`

is a Pandas series and `X`

and `Y`

and `Z`

, and all them share a common index.

This is basically that's a data frame, a series set which shares an index.

Until here that's OK. :)

I can grab all data values of W column greater than 0 of this way:

Note that C rows are disappear

But I don't understand the following:

What is the mean of this sentence?

df[df['W']>0][['Y','X']]

The result is this:

In theory, I am grabbing all data frame `W`

column values which be greater than 0 and I ahead that just return the `Y`

and `X`

columns based on what criteria or condition?

Why is the reason by which I grab these values on `Y`

and `X`

columns?

Currently, I am studying Pandas and I would like to know the reason for this behavior.

`df['W']>0`

returns a boolean series where the column is greater than zero (`true`

) else false`df[df['W']>0]`

returns all rows from the`df`

where the`df['W']>0`

is`true`

`df['X']`

returns the column 'X' of the dataframeSimilarly,

`df[['X', 'Y']]`

returns the columns`X`

&`Y`

from the dataframe

as you can see, the syntax `df[...]`

can take on different meanings:

- it can be used to mask the dataframe by rows by passing a boolean series the same length as the data frame
- it can be used to select a single column (pass in a string) or a group of column (pass in a list of strings)

**Indexing and selecting data,** iloc and loc are operations for retrieving data from Pandas dataframes. Selection and Indexing Methods for Pandas DataFrames. For these explorations we'll need Indexing in Pandas : Indexing in pandas means simply selecting particular rows and columns of data from a DataFrame. Indexing could mean selecting all the rows and some of the columns, some of the rows and all of the columns, or some of each of the rows and columns. Indexing can also be known as Subset Selection. Let’s see some example of indexing in Pandas.

When you do

df[df['W']>0]

a new `data frame`

is returned. Thus, when you put `[['Y', 'X']]`

in the end of this data frame, you're basically doing a simple selection in this new data frame. The values that will appear in column `X`

and `Y`

are just the values for `X`

and `Y`

of this df.

In more detail, `df['W']>0`

will return a `Boolean`

series, i.e. a series with values `True`

or `False`

. When you do `df[df['W']>0]`

you are filtering your `df`

using this series. The output will be rows of your `df`

where `df['W']>0`

returns `True`

.

**Indexing and Selecting Data,** Select Rows & Columns by Name or Index in DataFrame using loc & iloc | Python Pandas. Varun July 7, 2018 Select Rows & Columns by Name or Index in Python Pandas - Indexing and Selecting Data In this chapter, we will discuss how to slice and dice the date and generally get the subset of pandas object. The Python and NumPy indexing operators "[ ]" and attribute operator "." provide quick and easy access to Pandas data structures across a wide range of use cases.

As you explained in the 1st 2 steps:

- Returns Y, X columns

df[['Y','X']]

- Returns rows where W > 0

df[df['W']>0]

- Third step: Returns rows where W > 0 and then we select Y, X columns

df[df['W']>0][['Y','X']]

Basically first we apply function 1 to data frame, then on the output function 2 is applied. Hence the final output.

It's sequential execution of functions.

**Using iloc, loc, & ix to select rows and columns in Pandas DataFrames,** Python Pandas - Indexing and Selecting Data - In this chapter, we will discuss how library and aliasing as pd import pandas as pd import numpy as np df = pd. Pandas provide this feature through the use of DataFrames. A data frame consists of data, which is arranged in rows and columns, and row and column labels. You can easily select, slice or take a subset of the data in several different ways, for example by using labels, by index location, by value and so on.

It performs two independent operations in a oneliner.

- (Filtering rows)
`df[df['W'] > 0]`

selects only rows where the W column is positive - (Filtering columns)
`df[['X', 'Y']]`

selects only 2 columns of interest

**Select Rows & Columns by Name or Index in DataFrame using loc ,** Selecting data using Labels (Column Headings). We use square brackets [] to select a subset of a Python object. For example, we can select all data from a Pandas : Sort a DataFrame based on column names or row index labels using Dataframe.sort_index() Python Pandas : How to convert lists to a dataframe; Pandas : Loop or Iterate over all or certain columns of a dataframe; Python Pandas : Select Rows in DataFrame by conditions on multiple columns

If you compare this Python code to for example excel you could state:

IF(W>0,"Value if True(return Y and X)", "Value if False ("")")

**Python Pandas - Indexing and Selecting Data,** We set the column 'name' as our index. It is a common operation to pick out one of the DataFrame's columns to work on. To select a column by its label, we use Visit the post for more. Get The List Of Column Headers Or Name In Python Iloc loc and ix for data selection in python pandas how to get rows index names in pandas dataframe geeksforgeeks iloc loc and ix for data selection in python pandas iloc loc and ix for data selection in python pandas

**Indexing, Slicing and Subsetting DataFrames in Python – Data ,** 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 In this article, we will learn about pandas function of iterrows(), datareader() and operation for selecting rows by value.

**Pandas DataFrame Indexing Streamlined,** Selecting specific values of a pandas DataFrame or Series to work on is an implicit step in almost Native Python objects provide good ways of indexing data. 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.

**Selecting a row of pandas series/dataframe by integer index,** Selecting data from a pandas DataFrame. Linda Farczadi. The default indexing in pandas is always a numbering starting at 0 but we can change this to anything that we want,

##### Comments

- df[df['W']>0][['Y','X']] when W is more than 0 , getting the column of Y and X
`df[df['W']>0]`

returns a dataframe after applying your filter condition. Then`["Y", "X"]`

accesses the columns`Y`

and`X`

of that DataFrame.- The "proper" or idiomatic way to do this selection is to use .loc,
`df.loc[df['W']>0,['Y','X']]`

- @ScottBoston using .loc have some related with performance or memory when the selection operation is performed?
- @bgarcial You should reframe from chaining selections, in general. To spot chaining look for ']['. pandas.pydata.org/pandas-docs/stable/…