Selection of rows in pandas Dataframe

Question

I have a DataFrame say(df) in which columns are state , city , pincode.

I can select all the rows for particular city in a state like this.

requiredState = 'ABC'
requiredCity  = 'XYZ'

newDf = df[ (df['city']== requiredCity) & (df['state']==requiredState) ]

Now I want to select all the rows for all cities of a state.

So this can be done by simply removing the city condition like:

newDf = df[ (df['state'] == requiredState) ]

This will give me all the cities of a state.

My question is, Is there some method in which I need not to remove the city condition and it will result to all the cities in a state?

i.e. something like

requiredCity = ALL
nDf = df[ (df['city']== requiredCity) & (df['state']==requiredState) ]

or Can we pass a list of required cities instead of ALL?

i.e.

requiredCity = ['city1','city2']

thank you very much for edit.

jezrael
– jezrael

2018-11-12 11:04:05 +00:00
Commented Nov 12, 2018 at 11:04 — jezrael
– jezrael, Commented Nov 12, 2018 at 11:04
your welcome Jezrael. Keep sharing knowledge.

LMSharma
– LMSharma

2018-11-12 11:51:36 +00:00
Commented Nov 12, 2018 at 11:51 — LMSharma
– LMSharma, Commented Nov 12, 2018 at 11:51

jezrael · Accepted Answer · 2017-07-21 05:42:21Z

I think you need isin but requiredCity has to be in list, so is possible add new condition chained with | (or) of original condition:

df = pd.DataFrame({'city':list('abcdef'),
                   'pincode':[4,5,4,5,5,4],
                   'state':list('aaabbb')})

print (df)
  city  pincode state
0    a        4     a
1    b        5     a
2    c        4     a
3    d        5     b
4    e        5     b
5    f        4     b


requiredState = 'a'
requiredCity = ['ALL']

m1 = ('ALL' in requiredCity) | (df['city'].isin(requiredCity))
m2 = (df['state']==requiredState)
newDf = df[m1 & m2]
print (newDf)
  city  pincode state
0    a        4     a
1    b        5     a
2    c        4     a

requiredState = 'a'
requiredCity = ['a','c']

m1 = ('ALL' in requiredCity) | (df['city'].isin(requiredCity))
m2 = (df['state']==requiredState)
newDf = df[m1 & m2]
print (newDf)
  city  pincode state
0    a        4     a
2    c        4     a

requiredState = 'a'
requiredCity = ['a']

m1 = ('ALL' in requiredCity) | (df['city'].isin(requiredCity))
m2 = (df['state']==requiredState)
newDf = df[m1 & m2]
print (newDf)
  city  pincode state
0    a        4     a

ksai · Accepted Answer · 2017-07-21 05:28:26Z

1

 requiredCity = ['city1', 'city2']
 df[(df['city'].apply(lambda x : x in requiredCity)) & (df['state'] == requiredState)]

answered Jul 21, 2017 at 5:28

ksai

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