Filter a data frame using multiple conditions on various columns

There are instances where we are required to filter a data frame using multiple conditions on various columns.In this blog, we will look at how to accomplish that¶

In [2]:

import pandas as pd
import numpy as np

Step 1: Creating the data frame¶

In [5]:

day = ['Day1','Day1','Day1','Day1','Day2','Day2','Day2']
product=['A','B','C','D','A','B','C']
sales=np.random.rand(7)*10

In [6]:

df=pd.DataFrame([day,product,sales]).T
df.columns=['Day','Product','Sales']
df

Out[6]:

	Day	Product	Sales
0	Day1	A	6.856995
1	Day1	B	1.237694
2	Day1	C	0.958126
3	Day1	D	4.275079
4	Day2	A	5.684071
5	Day2	B	0.302415
6	Day2	C	6.963567

In [ ]:

df.dtypes

Step 2:Converting Sales into numeric data type¶

In [7]:

df['Sales']=df['Sales'].apply(pd.to_numeric,errors= "coerce")

Step 3: Filter on Day 1, Product A and Sales greater than 5¶

In [13]:

df.loc[(df['Day']=="Day1") & (df['Product'] == "A") & (df['Sales'] > 5),]

Out[13]:

	Day	Product	Sales
0	Day1	A	6.856995

Step 4: Filter on Day 1, Product A and B¶

In [16]:

df.loc[(df['Day']=="Day1") & (df['Product'].isin(["A","B"])) , ]

Out[16]:

	Day	Product	Sales
0	Day1	A	6.856995
1	Day1	B	1.237694