
Select rows from pandas DataFrame
Selecting the rows whose column values are equal to the given value in pandas:
import pandas
df = pandas.read_csv('SUB-IP-EST2019-ANNRNK.csv')
df2 = df.loc[df['City'] == 'New York city, New York']
print(df2)
# City 2010 ... 2018 2019
# 0 New York city, New York 8,190,209 ... 8,390,081 8,336,817
The data is the annual populations of the US cities (2010-2019).

Select multiple rows
import pandas
df = pandas.read_csv('SUB-IP-EST2019-ANNRNK.csv')
cities = ['New York city, New York', 'San Jose city, California']
df2 = df.loc[df['City'].isin(cities)]
print(df2)
# City 2010 ... 2018 2019
# 0 New York city, New York 8,190,209 ... 8,390,081 8,336,817
# 9 San Jose city, California 954,940 ... 1,028,020 1,021,795
To select multiple rows, use isin()
.
Select cities in Nevada
import pandas
df = pandas.read_csv('SUB-IP-EST2019-ANNRNK.csv')
df2 = df.loc[df['City'].str.contains('Nevada')]
print(df2)
# City 2010 2011 ... 2017 2018 2019
# 26 Las Vegas city, Nevada 584,576 586,606 ... 635,262 643,228 651,319
# 60 Henderson city, Nevada 257,452 258,752 ... 299,314 309,518 320,189
# 84 Reno city, Nevada 225,652 227,061 ... 246,615 250,146 255,601
# 88 North Las Vegas city, Nevada 216,836 218,291 ... 240,504 245,349 251,974
# 293 Sparks city, Nevada 91,237 91,616 ... 100,048 103,979 105,006
# 681 Carson City, Nevada 54,981 54,676 ... 54,532 55,202 55,916
str.contains('Nevada')
is selecting the values that contains Nevada
.
Select cities whose names don't contain 'city'
import pandas
df = pandas.read_csv('SUB-IP-EST2019-ANNRNK.csv')
df2 = df.loc[~df['City'].str.contains('city,')]
print(df2)
# City ... 2019
# 16 Indianapolis city (balance), Indiana ... 876,384
# 22 Nashville-Davidson metropolitan government (ba... ... 670,820
# 28 Louisville/Jefferson County metro government (... ... 617,638
# 55 Urban Honolulu CDP, Hawaii ... 345,064
# 59 Lexington-Fayette urban county, Kentucky ... 323,152
# 68 Anchorage municipality, Alaska ... 288,000
# 86 Gilbert town, Arizona ... 254,114
# 122 Augusta-Richmond County consolidated governmen... ... 197,888
# 153 Cary town, North Carolina ... 170,282
# 167 Macon-Bibb County, Georgia ... 153,159
The prefix ~
of df['City']
means the negation or excluding the rows satisfying the given conditions.
Source
Source: City and Town Population Totals: 2010-2019 (United States Census Bureau)
The data used in this article is a part of the original file.

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