Conditional Selection in Pandas

Conditional Selection

# Ask yourself: why is :9 is the correct slice to select the first 10 rows?
babynames_first_10_rows = babaynames.loc[:9, :]

# Notice how we have exactly 10 elements in our boolean array argument
babynames_first_10_rows[[True, False, True, False, True, False, True, False, True, False]]

 

To make things easier, we can instead provide a logical condition as an input to .loc or [ ] that returns a boolean array with the necessary length.

# First, use a logical condition to generate a boolean array
logical_operator = (babynames["Sex"] == "F")

# Then, use this boolean array to filter the DataFrame
babynames[logical_operator].head()

 

Here is a simple print code:

print("There are a total of {} values in 'logical_operator'".format(len(logical_operagtor)))

print("The 0th item in this 'logical_operator' is: {}".format(logical_operator.iloc[0]))
print("The 235790th item in this 'logical_operator' is: {}".format(logical_operator.iloc[235790]))
print("The 235791th item in this 'logical_operator' is: {}".format(logical_operator.iloc[235791]))

babynames.loc[babynames["Sex"] == "F"].head()

# if we want to return data on all females born before the 21st century, we can write:
babynames[(babynames["Sex"] == "F") & (babynames["Year"] < 2000)].head()

 

Boolean array selection is a useful tool, but can lead to overly verbose code for complex conditions. 

# Note: the parentheses surrounding the code make it possible to break the code on to multiple lines for readability
(
	babynames[	(babynames["Name"] == "Bella")	|
    			(babynames["Name"] == "Alex")	|
                (babynames["Name"] == "Ani")	|
                (babynames["Name"] == "Lisa")	|
).head()

 

.isin

# .isin function can be used to filter dataframes. select rows with having a particular value in a particular column
names = ["Bella", "Alex", "Ani", "Lisa"]
babynames[babynames["Name"].isin(names)].head()

 

str.startswith

# can be used to define a filter based on string values in a Series object
babynames[babynames["Name"].str.startswith("N")].head()