Pandas dataframe query multiple conditions

Pandas dataframe query multiple conditions DEFAULT

Pandas dataframe filter with Multiple conditions

Selecting or filtering rows from a dataframe can be sometime tedious if you don’t know the exact methods and how to filter rows with multiple conditions

In this post we are going to see the different ways to select rows from a dataframe using multiple conditions

Let’s create a dataframe with 5 rows and 4 columns i.e. Name, Age, Salary_in_1000 and FT_Team(Football Team)

Output:

-NameAgeSalary_in_1000FT_Team
0JOHN35100STEELERS
1ALLEN4293SEAHAWKS
2BOB6378FALCONS
3NIKI29120FALCONS
4CHARLIE4764PATRIOTS
5CHANG51115STEELERS

Selecting Dataframe rows on multiple conditions using these 5 functions

In this section we are going to see how to filter the rows of a dataframe with multiple conditions using these five methods

a) loc b) numpy where c) Query d) Boolean Indexing e) eval

What’s the Condition or Filter Criteria ?

Get all rows having salary greater or equal to 100K and Age < 60 and Favourite Football Team Name starts with ‘S’

Using loc with multiple conditions

loc is used to Access a group of rows and columns by label(s) or a boolean array

As an input to label you can give a single label or it’s index or a list of array of labels

Enter all the conditions and with & as a logical operator between them

Output:

NameFT_Team
0JOHNSTEELERS
5CHANGSTEELERS

Using np.where with multiple conditions

numpy where can be used to filter the array or get the index or elements in the array where conditions are met. You can read more about np.where in this post

Numpy where with multiple conditions and & as logical operators outputs the index of the matching rows

Output:

The output from the np.where, which is a list of row index matching the multiple conditions is fed to dataframe loc function

Output:

NameAgeSalary_in_1000FT_Team
0JOHN35100STEELERS
5CHANG51115STEELERS

Using Query with multiple Conditions

It is used to Query the columns of a DataFrame with a boolean expression

Output:

NameAgeSalary_in_1000
0JOHN35100
5CHANG51115

pandas boolean indexing multiple conditions

It is a standrad way to select the subset of data using the values in the dataframe and applying conditions on it

We are using the same multiple conditions here also to filter the rows from pur original dataframe with salary >= 100 and Football team starts with alphabet ‘S’ and Age is less than 60

Output:

NameAgeSalary_in_1000
0JOHN35100
5CHANG51115

Pandas Eval multiple conditions

Evaluate a string describing operations on DataFrame column. It Operates on columns only, not specific rows or elements

Output:

NameAgeSalary_in_1000
0JOHN35100
5CHANG51115

Conclusion:

In this post we have seen that what are the different methods which are available in the Pandas library to filter the rows and get a subset of the dataframe

And how these functions works: loc works with column labels and indexes, whereas eval and query works only with columns and boolean indexing works with values in a column only

Let me know your thoughts in the comments section below if you find this helpful or knows of any other functions which can be used to filter rows of dataframe using multiple conditions


Sours: https://kanoki.org/2020/01/21/pandas-dataframe-filter-with-multiple-conditions/

Filter Pandas Dataframe with multiple conditions

In this article, let’s discuss how to filter pandas dataframe with multiple conditions. There are possibilities of filtering data from Pandas dataframe with multiple conditions during the entire software development. The reason is dataframe may be having multiple columns and multiple rows. Selective display of columns with limited rows is always the expected view of users. To fulfill the user’s expectations and also help in machine deep learning scenarios, filtering of Pandas dataframe with multiple conditions is much necessary.

Let us see the different ways to do the same.

Creating a sample dataframe to proceed further

Python3

 

 

 

 

 

Output:



Method 1: Using loc

Here we will get all rows having Salary greater or equal to 100000 and Age < 40 and their JOB starts with ‘D’ from the dataframe. Print the details with Name and their JOB. For the above requirement, we can achieve this by using loc. It is used to access single or more rows and columns by label(s) or by a boolean array. loc works with column labels and indexes.

Python3

 

 

 

 

Output:

Output resolves for the given conditions and finally, we are going to show only 2 columns namely Name and JOB.

Method 2: Using NumPy



Here will get all rows having Salary greater or equal to 100000 and Age < 40 and their JOB starts with ‘D’ from the data frame. We need to use NumPy. 

Python3

 

 

 

 

 

Output:

In the above example, print(filtered_values) will give the output as (array([0], dtype=int64),)  which indicates the first row with index value 0 will be the output. After that output will have 1 row with all the columns and it is retrieved as per the given conditions.

Method 3: Using Query (eval and query works only with columns)

In this approach, we get all rows having Salary lesser or equal to 100000 and Age < 40, and their JOB starts with ‘C’ from the dataframe. Its just query the columns of a DataFrame with a single or more Boolean expressions and if multiple, it is having & condition in the middle.

Python3

 

 

 

 

 

Output:  



Method 4: pandas Boolean indexing multiple conditions standard way (“Boolean indexing” works with values in a column only)

In this approach, we get all rows having Salary lesser or equal to 100000 and Age < 40 and their JOB starts with ‘P’ from the dataframe. In order to select the subset of data using the values in the dataframe and applying Boolean conditions, we need to follow these ways

Python3

 

 

 

 

 

Output: 

We are mentioning a list of columns that need to be retrieved along with the Boolean conditions and since many conditions, it is having ‘&’.

Method 5: Eval multiple conditions  (“eval” and “query” works only with columns )

Here, we get all rows having Salary lesser or equal to 100000 and Age < 40 and their JOB starts with ‘A’ from the dataframe. 

Python3

 

 

 

 

 

Output:

Dataframes are a very essential concept in Python and filtration of data is required can be performed based on various conditions. They can be achieved in any one of the above ways. Ponts to be noted:

  • loc works with column labels and indexes.
  • eval and query works only with columns.
  • Boolean indexing works with values in a column only.

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Sours: https://www.geeksforgeeks.org/filter-pandas-dataframe-with-multiple-conditions/
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Filter Pandas DataFrame With Multiple Conditions

Created: January-16, 2021

  1. Filter Entries of a DataFrame Based on Multiple Conditions Using the Indexing
  2. Filter Entries of a DataFrame Based on Multiple Conditions Using the Method

This tutorial explains how we can filter entries from a DataFrame based on multiple conditions.

We will use the below DataFrame in this article.

Output:

Filter Entries of a DataFrame Based on Multiple Conditions Using the Indexing

Output:

It filters all the entries in the , whose value of the column is and the value of the column is less than 500.

We specify the conditions inside connecting the conditions using the or the operator to index the values based on multiple conditions. The operator represents the logic meaning both the conditions must be true to select an entry. The operator represents the logic meaning if any condition is satisfied to select the entry.

Filter Entries of a DataFrame Based on Multiple Conditions Using the Method

We pass the multiple conditions connected by the or the operator as an argument to the method.

Output:

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Related Article - Pandas Filter

  • Pandas Exclude Column
  • Sours: https://www.delftstack.com/howto/python-pandas/pandas-multiple-conditions/
    Python Pandas Tutorial 26 - How to Filter Pandas data frame for specific multiple values in a column
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    Sours: http://www.ritchieng.com/pandas-multi-criteria-filtering/

    Multiple conditions pandas dataframe query

    Python Pandas : Select Rows in DataFrame by conditions on multiple columns


    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,

    First let’s create a DataFrame,

    # List of Tuples students = [ ('jack', 'Apples' , 34) , ('Riti', 'Mangos' , 31) , ('Aadi', 'Grapes' , 30) , ('Sonia', 'Apples', 32) , ('Lucy', 'Mangos' , 33) , ('Mike', 'Apples' , 35) ] #Create a DataFrame object dfObj = pd.DataFrame(students, columns = ['Name' , 'Product', 'Sale'])
    Contents of DataFrame will be,
    Name Product Sale 0 jack Apples 34 1 Riti Mangos 31 2 Aadi Grapes 30 3 Sonia Apples 32 4 Lucy Mangos 33 5 Mike Apples 35
    Now let’s select rows from this DataFrame based on conditions,

    Select Rows based on value in column

    Select rows in above DataFrame for which ‘Product’ column contains the value ‘Apples’,

    subsetDataFrame = dfObj[dfObj['Product'] == 'Apples']
    It will return a DataFrame in which Column ‘Product‘ contains ‘Apples‘ only i.e.
    Name Product Sale 0 jack Apples 34 3 Sonia Apples 32 5 Mike Apples 35
    How does that work internally ?

    Applying condition on a DataFrame like this,

    dfObj['Product'] == 'Apples'
    Will return a Series objectof True & False i.e.
    0 True 1 False 2 False 3 True 4 False 5 True Name: Product, dtype: bool
    Series will contain Truewhen condition is passed and Falsein other cases.

    If we pass this series object to [] operator of DataFrame, then it will return a new DataFrame with only those rows that has True in the passed Series object i.e.

    dfObj[dfObj['Product'] == 'Apples']
    It will return a DataFrame in which Column passed series object had True entry i.e.
    DataFrame with Product : Apples Name Product Sale 0 jack Apples 34 3 Sonia Apples 32 5 Mike Apples 35

    Select Rows based on any of the multiple values in column

    Select rows in above DataFrame for which ‘Product‘ column contains either ‘GrapesorMangos‘ i.e

    subsetDataFrame = dfObj[dfObj['Product'].isin(['Mangos', 'Grapes']) ]
    We have passed a list of product names in isin() function of DataFrame that will return Truefor each entry in ‘Product‘ column that matches with any entry in given list.
    Therefore, it will return a DataFrame in which Column ‘Product‘ contains either ‘Grapes‘ or ‘Mangos‘ only i.e.
    Name Product Sale 1 Riti Mangos 31 2 Aadi Grapes 30 4 Lucy Mangos 33

    Select DataFrame Rows Based on multiple conditions on columns

    Select rows in above DataFrame for which ‘Sale’ column contains Values greater than 30 & less than 33 i.e.

    filterinfDataframe = dfObj[(dfObj['Sale'] > 30) & (dfObj['Sale'] < 33) ]
    It will return following DataFrame object in which Salescolumn  contains value between 31 to 32,
    Name Product Sale 1 Riti Mangos 31 3 Sonia Apples 32
    Complete example is as follows,
    import pandas as pd def main(): # List of Tuples students = [ ('jack', 'Apples' , 34) , ('Riti', 'Mangos' , 31) , ('Aadi', 'Grapes' , 30) , ('Sonia', 'Apples', 32) , ('Lucy', 'Mangos' , 33) , ('Mike', 'Apples' , 35) ] #Create a DataFrame object dfObj = pd.DataFrame(students, columns = ['Name' , 'Product', 'Sale']) print("Original Dataframe" , dfObj, sep='\n') ''' Select Rows based on value in a column ''' subsetDataFrame = dfObj[dfObj['Product'] == 'Apples'] print("DataFrame with Product : Apples" , subsetDataFrame, sep='\n') filteringSeries = dfObj['Product'] == 'Apples' print("Filtering Series" , filteringSeries, sep='\n') subsetDataFrame = dfObj[filteringSeries] print("DataFrame with Product : Apples" , subsetDataFrame, sep='\n') ''' Select Rows based on any of the multiple values in a column ''' subsetDataFrame = dfObj[dfObj['Product'].isin(['Mangos', 'Grapes']) ] print("DataFrame with Product : Mangos or Grapes" , subsetDataFrame, sep='\n') ''' Select DataFrame Rows Based on multiple conditions on columns ''' filterinfDataframe = dfObj[(dfObj['Sale'] > 30) & (dfObj['Sale'] < 33) ] print("DataFrame with Sales between 31 to 32" , filterinfDataframe, sep='\n') if __name__ == '__main__': main()
    Output:
    Original Dataframe Name Product Sale 0 jack Apples 34 1 Riti Mangos 31 2 Aadi Grapes 30 3 Sonia Apples 32 4 Lucy Mangos 33 5 Mike Apples 35 DataFrame with Product : Apples Name Product Sale 0 jack Apples 34 3 Sonia Apples 32 5 Mike Apples 35 Filtering Series 0 True 1 False 2 False 3 True 4 False 5 True Name: Product, dtype: bool DataFrame with Product : Apples Name Product Sale 0 jack Apples 34 3 Sonia Apples 32 5 Mike Apples 35 DataFrame with Product : Mangos or Grapes Name Product Sale 1 Riti Mangos 31 2 Aadi Grapes 30 4 Lucy Mangos 33 DataFrame with Sales between 31 to 32 Name Product Sale 1 Riti Mangos 31 3 Sonia Apples 32
     

     

    Sours: https://thispointer.com/python-pandas-select-rows-in-dataframe-by-conditions-on-multiple-columns/
    Selecting Rows in Pandas DataFrame Based on Conditions - GeeksforGeeks

    Pandas dataframe filter with multiple conditions

    Let's take a look at a few different ways to filter and select rows in a pandas dataframe based on multiple conditions.

    To start we're going to create a simple dataframe in python:

    6 functions to filter dataframe rows on multiple conditions

    Let's look at six different ways to filter rows in a dataframe based on multiple conditions:

    What conditions do we want to filter on?

    Get all rows having hourly wage greater than or equal to 100 and age < 60 and favorite football team name starts with ‘S’.

    Using Loc to Filter With Multiple Conditions

    The loc function in pandas can be used to access groups of rows or columns by label.

    Add each condition you want to be included in the filtered result and concatenate them with the & operator. You'll see our code sample will return a pd.dataframe of our filtered rows. Don't forget to include "import pandas as pd" at the top!

    Using The Numpy Function np.where To Filter With Multiple Conditions

    The Numpy python library interacts great with dataframes, especially when dealing with indexing.

    Let's use the numpy.where function with a few conditions to produce the same result we wanted above.

    Let's take the returned row index list that matches the combined conditions into loc

    Using The Pandas Function Query With Multiple Conditions

    We can use the pandas dataframe function query() and boolean expressions to get our filtered rows back. The main benefit of the query function is it uses numexpr which improves efficiency, especially in larger dataframes.

    Filtering On Multiple Conditions Using Pandas Boolean Indexing

    This is a good method to go with if you want to remove columns as well, as you can exclude any dataframe columns you don't want in the last statement. Boolean indexing is also very efficient as it does not make a copy of the data.

    Pandas Eval Function With Multiple Conditions

    We can use the pandas dataframe function eval inside a df[] tag to filter on these conditions. Note the .values() at the end.

    Looking To Filter Where Only 1 Condition Has To Be True?

    This numpy array and functools implementation allows you to use disjunction to filter your dataframe. Here's an example:

    Note: you still need "import pandas as pd"

    Dataframe Comparison Tools For Multiple Condition Filtering

    Post pandas .22 update, there's multiple functions you can use as well to compare column values to conditions. Make sure your dtype is the same as what you want to compare to.

    Sours: https://www.scalr.ai/pandas/pandas-dataframe-filter-multiple-conditions

    You will also like:

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    Sours: https://www.codegrepper.com/code-examples/python/select+rows+with+multiple+conditions+pandas+query


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