import pandas as pd
filtered_df = dataframe.query('your_query')dataframe: Replace this with the name of the pandas DataFrame you want to filter.
Examples
Basic Example
filtered_df = df.query('age > 25')Multiple Conditions
filtered_df = df.query('age > 25 and gender == "Male"')Using Variables
age_threshold = 25
gender_value = 'Male'
filtered_df = df.query('age > @age_threshold and gender == @gender_value')Import Data
Make sure pandas package is already installed before submitting the following code. You can check it by running !pip show pandas statement in Ipython console. If it is not installed, you can install it by using the command !pip install pandas.
We are going to use dataset containing details of flights departing from NYC in 2013. This dataset has 336776 rows and 16 columns. See column names below. To import dataset, we are using read_csv( ) function from pandas package.
['year', 'month', 'day', 'dep_time', 'dep_delay', 'arr_time',
'arr_delay', 'carrier', 'tailnum', 'flight', 'origin', 'dest',
'air_time', 'distance', 'hour', 'minute']import pandas as pd
df = pd.read_csv("<https://raw.githubusercontent.com/JackyP/testing/master/datasets/nycflights.csv>", usecols=range(1,17))Filter pandas dataframe by column value
Condition : Select flights details of JetBlue Airways that has 2 letters carrier code B6 with origin from JFK airport
Method 1 : DataFrame Way
newdf = df[(df.origin == "JFK") & (df.carrier == "B6")]newdf.head()Output
Out[23]:
year month day dep_time ... air_time distance hour minute
3 2013 1 1 544.0 ... 183.0 1576 5.0 44.0
8 2013 1 1 557.0 ... 140.0 944 5.0 57.0
10 2013 1 1 558.0 ... 149.0 1028 5.0 58.0
11 2013 1 1 558.0 ... 158.0 1005 5.0 58.0
15 2013 1 1 559.0 ... 44.0 187 5.0 59.0
[5 rows x 16 columns]- Filtered data (after subsetting) is stored on new dataframe called
newdf. - Symbol
&refers toANDcondition which means meeting both the criteria. - This part of code
(df.origin == "JFK") & (df.carrier == "B6")returns True / False. True where condition matches and False where the condition does not hold. Later it is passed within df and returns all the rows corresponding to True. It returns 4166 rows.
Method 2 : Query Function
In pandas package, there are multiple ways to perform filtering. The above code can also be written like the code shown below. This method is elegant and more readable and you don’t need to mention dataframe name everytime when you specify columns (variables).
newdf = df.query('origin == "JFK" & carrier == "B6"')Method 3 : loc function
loc is an abbreviation of location term. All these 3 methods return same output. It’s just a different ways of doing filtering rows.
newdf = df.loc[(df.origin == "JFK") & (df.carrier == "B6")]Filter Pandas Dataframe by Row and Column Position
Suppose you want to select specific rows by their position (let’s say from second through fifth row). We can use df.iloc[ ] function for the same.
Indexing in python starts from zero. df.iloc[0:5,] refers to first to fifth row (excluding end point 6th row here). df.iloc[0:5,] is equivalent to df.iloc[:5,]
df.iloc[:5,] #First 5 rows
df.iloc[1:5,] #Second to Fifth row
df.iloc[5,0] #Sixth row and 1st column
df.iloc[1:5,0] #Second to Fifth row, first column
df.iloc[1:5,:5] #Second to Fifth row, first 5 columns
df.iloc[2:7,1:3] #Third to Seventh row, 2nd and 3rd columnDifference between loc and iloc function
loc considers rows based on index labels. Whereas iloc considers rows based on position in the index so it only takes integers.
Let’s create a sample data for illustration
import numpy as np
x = pd.DataFrame({"col1" : np.arange(1,20,2)}, index=[9,8,7,6,0, 1, 2, 3, 4, 5])Output
col1
9 1
8 3
7 5
6 7
0 9
1 11
2 13
3 15
4 17
5 19iloc – Index Position
x.iloc[0:5]Output
col1
9 1
8 3
7 5
6 7
0 9Selecting rows based on index or row position
loc – Index Label
x.loc[0:5]Output
col1
0 9
1 11
2 13
3 15
4 17
5 19Selecting rows based on labels of index
How x.loc[0:5] returns 6 rows (inclusive of 5 which is 6th element)?
It is because loc does not produce output based on index position. It considers labels of index only which can be alphabet as well and includes both starting and end point. Refer the example below.
x = pd.DataFrame({"col1" : range(1,5)}, index=['a','b','c','d'])
x.loc['a':'c'] # equivalent to x.iloc[0:3]Output
col1
a 1
b 2
c 3Filter pandas dataframe by rows position and column names
Here we are selecting first five rows of two columns named origin and dest.
df.loc[df.index[0:5],["origin","dest"]]df.index returns index labels. df.index[0:5] is required instead of 0:5 (without df.index) because index labels do not always in sequence and start from 0. It can start from any number or even can have alphabet letters. Refer the example where we showed comparison of iloc and loc.
Selecting multiple values of a column
Suppose you want to include all the flight details where origin is either JFK or LGA.
# Long Way
newdf = df.loc[(df.origin == "JFK") | (df.origin == "LGA")]
# Smart Way
newdf = df[df.origin.isin(["JFK", "LGA"])]| implies OR condition which means any of the conditions holds True. isin( ) is similar to IN operator in SAS and R which can take many values and apply OR condition. Make sure you specify values in list [ ].
Select rows whose column value does not equal a specific value
In this example, we are deleting all the flight details where origin is from JFK. != implies NOT EQUAL TO.
newdf = df.loc[(df.origin != "JFK") & (df.carrier == "B6")]Let’s check whether the above line of code works fine or not by looking at unique values of column origin in newdf.
pd.unique(newdf.origin)
# ['LGA', 'EWR']How to negate the whole condition
Tilde ~ is used to negate the condition. It is equivalent to NOT operator in SAS and R.
newdf = df[~((df.origin == "JFK") & (df.carrier == "B6"))]Select Non-Missing Data in Pandas Dataframe
With the use of notnull() function, you can exclude or remove NA and NAN values. In the example below, we are removing missing values from origin column. Since this dataframe does not contain any blank values, you would find same number of rows in newdf.
newdf = df[df.origin.notnull()]Filtering String in Pandas Dataframe
It is generally considered tricky to handle text data. But python makes it easier when it comes to dealing character or string columns. Let’s prepare a fake data for example.
import pandas as pd
df = pd.DataFrame({"var1": ["AA_2", "B_1", "C_2", "A_2"]})Output
var1
0 AA_2
1 B_1
2 C_2
3 A_2Select rows having values starting from letter ‘A’
By using .str, you can enable string functions and can apply on pandas dataframe. str[0] means first letter.
df[df['var1'].str[0] == 'A']Filter rows having string length greater than 3
len( ) function calculates length of iterable.
df[df['var1'].str.len()>3]Select string containing letters A or B
contains( ) function is similar to LIKE statement in SQL and SAS. You can subset data by mentioning pattern in contains( ) function.
df[df['var1'].str.contains('A|B')]Output
var1
0 AA_2
1 B_1
3 A_2Handle space in column name while filtering
Let’s rename a column var1 with a space in between var 1 We can rename it by using rename function.
df.rename(columns={'var1':'var 1'}, inplace = True)By using backticks we can include the column having space. See the example code below.
newdf = df.query("`var 1` == 'AA_2'")Backticks are supported from version 0.25 of pandas package. Run this command in console to check pandas version !pip show pandas If you have version prior to the version 0.25 you can upgrade it by using this command !pip install --upgrade pandas --user
How to filter data without using pandas package
You can perform filtering using pure python methods without dependency on pandas package.
Warning : Methods shown below for filtering are not efficient ones. The main objective of showing the following methods is to show how to do subsetting without using pandas package. In your live project, you should use pandas’ builtin functions (query( ), loc[ ], iloc[ ]) which are explained above.
We don’t need to create a dataframe to store data. We can stock it in list data structure. lst_df contains flights data which were imported from CSV file.
import csv
import requests
response = requests.get('<https://dyurovsky.github.io/psyc201/data/lab2/nycflights.csv>').text
lines = response.splitlines()
d = csv.DictReader(lines)
lst_df = list(d)Lambda Method for Filtering
Lambda is an alternative way of defining user defined function. With the use of lambda, you can define function in a single line of code. You can check out this link to learn more about it.
l1 = list(filter(lambda x: x["origin"] == 'JFK' and x["carrier"] == 'B6', lst_df))If you are wondering how to use this lambda function on a dataframe, you can submit the code below.
newdf = df[df.apply(lambda x: x["origin"] == 'JFK' and x["carrier"] == 'B6', axis=1)]List Comprehension Method for Filtering
List comprehension is an alternative to lambda function and makes code more readable.
l2 = list(x for x in lst_df if x["origin"] == 'JFK' and x["carrier"] == 'B6')You can use list comprehension on dataframe like the way shown below.
newdf = df.iloc[[index for index,row in df.iterrows() if row['origin'] == 'JFK' and row['carrier'] == 'B6']]Create Class for Filtering
Python is an object-oriented programming language in which code is implemented using class.