4.10. DataFrame Query

4.10.1. Rationale

df[df['sales'] > 50000]
df.query('sales > 50000')

Figure 4.11. Pandas query expression [sharpsightlabs]

4.10.2. Query

import pandas as pd

df = pd.DataFrame({
    'name': ['William','Emma','Sofia','Markus','Edward','Thomas','Ethan','Olivia','Arun','Anika','Paulo'],
    'region': ['East','North','East','South','West','West','South','West','West','East','South'],
    'sales': [50000,52000,90000,34000,42000,72000,49000,55000,67000,65000,67000],
    'expenses': [42000,43000,50000,44000,38000,39000,42000,60000,39000,44000,45000]})

df
#        name region  sales  expenses
# 0   William   East  50000     42000
# 1      Emma  North  52000     43000
# 2     Sofia   East  90000     50000
# 3    Markus  South  34000     44000
# 4    Edward   West  42000     38000
# 5    Thomas   West  72000     39000
# 6     Ethan  South  49000     42000
# 7    Olivia   West  55000     60000
# 8      Arun   West  67000     39000
# 9     Anika   East  65000     44000
# 10    Paulo  South  67000     45000

Code 4.81. Subset a pandas dataframe based on a numeric variable

df.query('sales > 60000')
#       name region  sales  expenses
# 2    Sofia   East  90000     50000
# 5   Thomas   West  72000     39000
# 8     Arun   West  67000     39000
# 9    Anika   East  65000     44000
# 10   Paulo  South  67000     45000

Code 4.82. Select rows based on a categorical variable

df.query('region == "East"')
#       name region  sales
# 0  William   East  50000
# 2    Sofia   East  90000
# 9    Anika   East  65000

Code 4.83. Subset a dataframe by index

df.query('index < 3')
#       name region  sales  expenses
# 0  William   East  50000     42000
# 1     Emma  North  52000     43000
# 2    Sofia   East  90000     50000

Code 4.84. Every odd index

df.query('index%2 == 1')
#          name region  sales  expenses
# 1    Emma  North  52000     43000
# 3  Markus  South  34000     44000
# 5  Thomas   West  72000     39000
# 7  Olivia   West  55000     60000
# 9   Anika   East  65000     44000

Code 4.85. Subset a pandas dataframe by comparing two columns

df.query('sales < expenses')
#      name region  sales  expenses
# 3  Markus  South  34000     44000
# 7  Olivia   West  55000     60000

Code 4.86. Subset a pandas dataframe with multiple conditions

df.query('(sales > 50000) and (region in ["East", "West"])')
#          name region  sales  expenses
# 2   Sofia   East  90000     50000
# 5  Thomas   West  72000     39000
# 7  Olivia   West  55000     60000
# 8    Arun   West  67000     39000
# 9   Anika   East  65000     44000

Code 4.87. Reference local variables inside of query

mean = df['sales'].mean()

mean
# 58454.545

df.query('sales > @mean')
#       name region  sales  expenses
# 2    Sofia   East  90000     50000
# 5   Thomas   West  72000     39000
# 8     Arun   West  67000     39000
# 9    Anika   East  65000     44000
# 10   Paulo  South  67000     45000

Code 4.88. Modify a dataframe in place

df2 = df.copy()
df2.query('index < 5', inplace = True)

print(df2)
#       name region  sales  expenses
# 0  William   East  50000     42000
# 1     Emma  North  52000     43000
# 2    Sofia   East  90000     50000
# 3   Markus  South  34000     44000
# 4   Edward   West  42000     38000

4.10.3. References

• Source: https://www.sharpsightlabs.com/blog/pandas-query/

sharpsightlabs

https://www.sharpsightlabs.com/blog/pandas-query/