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Keyword categorization from strings in a new column in pandas

This is not the best approach but this what I did so far:
I have this example df:
df = pd.DataFrame({
'City': ['I lived Los Angeles', 'I visited London and Toronto','the best one is Toronto', 'business hub is in New York',' Mexico city is stunning']
})
df
gives:
City
0 I lived Los Angeles
1 I visited London and Toronto
2 the best one is Toronto
3 business hub is in New York
4 Mexico city is stunning
I am trying to match (case insensitive) city names from a nested dic and create a new column with the country name with int values for statistical purposes.
So, here is my nested dic as a reference for countries and cities:
country = { 'US': ['New York','Los Angeles','San Diego'],
'CA': ['Montreal','Toronto','Manitoba'],
'UK': ['London','Liverpool','Manchester']
}
and I created a function that should look for the city from the df and match it with the dic, then create a column with the country name:
def get_country(x):
count = 0
for k,v in country.items():
for y in v:
if y.lower() in x:
df[k] = count + 1
else:
return None
then applied it to df:
df.City.apply(lambda x: get_country(x.lower()))
I got the following output:
City US
0 I lived Los Angeles 1
1 I visited London and Toronto 1
2 the best one is Toronto 1
3 business hub is in New York 1
4 Mexico city is stunning 1
Expected output:
City US CA UK
0 I lived Los Angeles 1 0 0
1 I visited London and Toronto 0 1 1
2 the best one is Toronto 0 1 0
3 business hub is in New York 1 0 0
4 Mexico city is stunning 0 0 0

Here is a solution based on your function. I changed the name of the variables to be more readable and easy to follow.
df = pd.DataFrame({
'City': ['I lived Los Angeles',
'I visited London and Toronto',
'the best one is Toronto',
'business hub is in New York',
' Mexico city is stunning']
})
country_cities = {
'US': ['New York','Los Angeles','San Diego'],
'CA': ['Montreal','Toronto','Manitoba'],
'UK': ['London','Liverpool','Manchester']
}
def get_country(text):
text = text.lower()
count = 0
country_counts = dict.fromkeys(country_cities, 0)
for country, cities in country_cities.items():
for city in cities:
if city.lower() in text:
country_counts[country] += 1
return pd.Series(country_counts)
df = df.join(df.City.apply(get_country))
Output:
City US CA UK
0 I lived Los Angeles 1 0 0
1 I visited London and Toronto 0 1 1
2 the best one is Toronto 0 1 0
3 business hub is in New York 1 0 0
4 Mexico city is stunning 0 0 0
Solution based on Series.str.count
A simpler solution is using Series.str.count to count the occurences of the following regex pattern city1|city2|etc for each country (the pattern matches city1 or city2 or etc). Using the same setup as above:
country_patterns = {country: '|'.join(cities) for country, cities in country_cities.items()}
for country, pat in country_patterns.items():
df[country] = df['City'].str.count(pat)
Why your solution doesn't work?
if y.lower() in x:
df[k] = count + 1
else:
return None
The reason your function doesn't produce the right output is that
you are returning None if a city is not found in the text: the remaining countries and cities are not checked, because the return statement automatically exits the function.
What is happening is that only US cities are checked, and the line df[k] = 1 (in this case k = 'US') creates an entire column named k filled with the value 1. It's not creating a single value for that row, it creates or modifies the full column. When using apply you want to change a single row or value (the input of function), so don't change directly the main DataFrame inside the function.

You can achieve this result using a lambda function to check if any city for each country is contained in the string, after first lower-casing the city names in country:
cl = { k : list(map(str.lower, v)) for k, v in country.items() }
for ctry, cities in cl.items():
df[ctry] = df['City'].apply(lambda s:any(c in s.lower() for c in cities)).astype(int)
Output:
City US CA UK
0 I lived Los Angeles 1 0 0
1 I visited London and Toronto 0 1 1
2 the best one is Toronto 0 1 0
3 business hub is in New York 1 0 0
4 Mexico city is stunning 0 0 0

Function to move specific row to top or bottom of pandas dataframe

I have two functions which shift a row of a pandas dataframe to the top or bottom, respectively. After applying them more then once to a dataframe, they seem to work incorrectly.
These are the 2 functions to move the row to top / bottom:
def shift_row_to_bottom(df, index_to_shift):
"""Shift row, given by index_to_shift, to bottom of df."""
idx = df.index.tolist()
idx.pop(index_to_shift)
df = df.reindex(idx + [index_to_shift])
return df
def shift_row_to_top(df, index_to_shift):
"""Shift row, given by index_to_shift, to top of df."""
idx = df.index.tolist()
idx.pop(index_to_shift)
df = df.reindex([index_to_shift] + idx)
return df
Note: I don't want to reset_index for the returned df.
Example:
df = pd.DataFrame({'Country' : ['USA', 'GE', 'Russia', 'BR', 'France'],
'ID' : ['11', '22', '33','44', '55'],
'City' : ['New-York', 'Berlin', 'Moscow', 'London', 'Paris'],
'short_name' : ['NY', 'Ber', 'Mosc','Lon', 'Pa']
})
df =
Country ID City short_name
0 USA 11 New-York NY
1 GE 22 Berlin Ber
2 Russia 33 Moscow Mosc
3 BR 44 London Lon
4 France 55 Paris Pa
This is my dataframe:
Now, apply function for the first time. Move row with index 0 to bottom:
df_shifted = shift_row_to_bottom(df,0)
df_shifted =
Country ID City short_name
1 GE 22 Berlin Ber
2 Russia 33 Moscow Mosc
3 BR 44 London Lon
4 France 55 Paris Pa
0 USA 11 New-York NY
The result is exactly what I want.
Now, apply function again. This time move row with index 2 to the bottom:
df_shifted = shift_row_to_bottom(df_shifted,2)
df_shifted =
Country ID City short_name
1 GE 22 Berlin Ber
2 Russia 33 Moscow Mosc
4 France 55 Paris Pa
0 USA 11 New-York NY
2 Russia 33 Moscow Mosc
Well, this is not what I was expecting. There must be a problem when I want to apply the function a second time. The promblem is analog to the function shift_row_to_top.
My question is:
What's going on here?
Is there a better way to shift a specific row to top / bottom of the dataframe? Maybe a pandas-function?
If not, how would you do it?

Your problem is these two lines:
idx = df.index.tolist()
idx.pop(index_to_shift)
idx is a list and idx.pop(index_to_shift) removes the item at index index_to_shift of idx, which is not necessarily valued index_to_shift as in the second case.
Try this function:
def shift_row_to_bottom(df, index_to_shift):
idx = [i for i in df.index if i!=index_to_shift]
return df.loc[idx+[index_to_shift]]
# call the function twice
for i in range(2): df = shift_row_to_bottom(df, 2)
Output:
Country ID City short_name
0 USA 11 New-York NY
1 GE 22 Berlin Ber
3 BR 44 London Lon
4 France 55 Paris Pa
2 Russia 33 Moscow Mosc

Python split one column into multiple columns and reattach the split columns into original dataframe

I want to split one column from my dataframe into multiple columns, then attach those columns back to my original dataframe and divide my original dataframe based on whether the split columns include a specific string.
I have a dataframe that has a column with values separated by semicolons like below.
import pandas as pd
data = {'ID':['1','2','3','4','5','6','7'],
'Residence':['USA;CA;Los Angeles;Los Angeles', 'USA;MA;Suffolk;Boston', 'Canada;ON','USA;FL;Charlotte', 'NA', 'Canada;QC', 'USA;AZ'],
'Name':['Ann','Betty','Carl','David','Emily','Frank', 'George'],
'Gender':['F','F','M','M','F','M','M']}
df = pd.DataFrame(data)
Then I split the column as below, and separated the split column into two based on whether it contains the string USA or not.
address = df['Residence'].str.split(';',expand=True)
country = address[0] != 'USA'
USA, nonUSA = address[~country], address[country]
Now if you run USA and nonUSA, you'll note that there are extra columns in nonUSA, and also a row with no country information. So I got rid of those NA values.
USA.columns = ['Country', 'State', 'County', 'City']
nonUSA.columns = ['Country', 'State']
nonUSA = nonUSA.dropna(axis=0, subset=[1])
nonUSA = nonUSA[nonUSA.columns[0:2]]
Now I want to attach USA and nonUSA to my original dataframe, so that I will get two dataframes that look like below:
USAdata = pd.DataFrame({'ID':['1','2','4','7'],
'Name':['Ann','Betty','David','George'],
'Gender':['F','F','M','M'],
'Country':['USA','USA','USA','USA'],
'State':['CA','MA','FL','AZ'],
'County':['Los Angeles','Suffolk','Charlotte','None'],
'City':['Los Angeles','Boston','None','None']})
nonUSAdata = pd.DataFrame({'ID':['3','6'],
'Name':['David','Frank'],
'Gender':['M','M'],
'Country':['Canada', 'Canada'],
'State':['ON','QC']})
I'm stuck here though. How can I split my original dataframe into people whose Residence include USA or not, and attach the split columns from Residence ( USA and nonUSA ) back to my original dataframe?
(Also, I just uploaded everything I had so far, but I'm curious if there's a cleaner/smarter way to do this.)

There is unique index in original data and is not changed in next code for both DataFrames, so you can use concat for join together and then add to original by DataFrame.join or concat with axis=1:
address = df['Residence'].str.split(';',expand=True)
country = address[0] != 'USA'
USA, nonUSA = address[~country], address[country]
USA.columns = ['Country', 'State', 'County', 'City']
nonUSA = nonUSA.dropna(axis=0, subset=[1])
nonUSA = nonUSA[nonUSA.columns[0:2]]
#changed order for avoid error
nonUSA.columns = ['Country', 'State']
df = pd.concat([df, pd.concat([USA, nonUSA])], axis=1)
Or:
df = df.join(pd.concat([USA, nonUSA]))
print (df)
ID Residence Name Gender Country State \
0 1 USA;CA;Los Angeles;Los Angeles Ann F USA CA
1 2 USA;MA;Suffolk;Boston Betty F USA MA
2 3 Canada;ON Carl M Canada ON
3 4 USA;FL;Charlotte David M USA FL
4 5 NA Emily F NaN NaN
5 6 Canada;QC Frank M Canada QC
6 7 USA;AZ George M USA AZ
County City
0 Los Angeles Los Angeles
1 Suffolk Boston
2 NaN NaN
3 Charlotte None
4 NaN NaN
5 NaN NaN
6 None None
But it seems it is possible simplify:
c = ['Country', 'State', 'County', 'City']
df[c] = df['Residence'].str.split(';',expand=True)
print (df)
ID Residence Name Gender Country State \
0 1 USA;CA;Los Angeles;Los Angeles Ann F USA CA
1 2 USA;MA;Suffolk;Boston Betty F USA MA
2 3 Canada;ON Carl M Canada ON
3 4 USA;FL;Charlotte David M USA FL
4 5 NA Emily F NA None
5 6 Canada;QC Frank M Canada QC
6 7 USA;AZ George M USA AZ
County City
0 Los Angeles Los Angeles
1 Suffolk Boston
2 None None
3 Charlotte None
4 None None
5 None None
6 None None

String mode aggregation with group by function

I have dataframe which looks like below
Country City
UK London
USA Washington
UK London
UK Manchester
USA Washington
USA Chicago
I want to group country and aggregate on the most repeated city in a country
My desired output should be like
Country City
UK London
USA Washington
Because London and Washington appears 2 times whereas Manchester and Chicago appears only 1 time.
I tried
from scipy.stats import mode
df_summary = df.groupby('Country')['City'].\
apply(lambda x: mode(x)[0][0]).reset_index()
But it seems it won't work on strings

I can't replicate your error, but you can use pd.Series.mode, which accepts strings and returns a series, using iat to extract the first value:
res = df.groupby('Country')['City'].apply(lambda x: x.mode().iat[0]).reset_index()
print(res)
Country City
0 UK London
1 USA Washington

try like below:
>>> df.City.mode()
0 London
1 Washington
dtype: object
OR
import pandas as pd
from scipy import stats
Can use scipy with stats + lambda :
df.groupby('Country').agg({'City': lambda x:stats.mode(x)[0]})
City
Country
UK London
USA Washington
# df.groupby('Country').agg({'City': lambda x:stats.mode(x)[0]}).reset_index()
However, it gives nice count as well if you don't want to return ony First value:
>>> df.groupby('Country').agg({'City': lambda x:stats.mode(x)})
City
Country
UK ([London], [2])
USA ([Washington], [2])

Extracting Lat/Long from returned json via google api(Geocoding)

I have a definition which looks like the below block. I think I'm experiencing the same problem. I think the api has updated, and thus the extraction of the lat/long coordinates may be in a slightly different position. I have made requests successfully by inputing an example address in as a parameter, but I can't get that to work in my def(http://docs.python-requests.org/en/master/user/quickstart/#make-a-request). I want my definition to return the lat/longs from the address using a for loop. I'm unfamiliar with parsing json:/ Any help appreciated!
Also, would geocode_result need to be json_results from my request results codeblock?
def geocode_address(loc):
gmaps = googlemaps.Client(key= creds.GOOGLE_MAPS['api_key'])
geocode_result = gmaps.geocode(loc)
lat = json_results[0]["geometry"]["location"]["lat"]
lon = json_results[0]["geometry"]["location"]["lng"]
print (lat,lon)

I don't see a difference between what this does and what your code does. Hope it's of some use to you.
>>> import requests
>>> payload = {'key':}
>>> base_url = 'https://maps.googleapis.com/maps/api/geocode/json'
>>> payload = {'address': '1845 E. Broadway Road Ste. 102, Tempe, AE, 85282'}
>>> r = requests.get(base_url, params=payload)
>>> r
<Response [200]>
>>> coords = r.json()['results'][0]['geometry']['location']
>>> coords['lat']
33.406601
>>> coords['lng']
-111.9075196
EDIT:
Start with a dataframe of two columns, one with the names of the veterinary hospitals, one with their addresses.
>>> import pandas as pd
>>> df
0 \
0 The Animal Clinic
1 Sherbourne Animal Hospital
2 Spadina Animal Hospital
3 Wellesley Animal Hospital
4 Cabbagetown Pet Clinic
1
0 106 Mutual St Toronto Ontario M5B 2R7
1 320 Richmond Street East Unit 8 Toronto Onta...
2 125 Spadina Avenue Toronto Ontario M5V 2K8
3 8 Wellesley St W Toronto Ontario M4Y 1E7
4 239 Gerrard St E Toronto Ontario M5A 2G1
Use .tolist() to obtain the addresses in the form of a list so that they can be passed one at a time to google for their latitudes and longitudes which are stored in the eponymous lists. Display the results.
>>> import requests
>>> base_url = 'https://maps.googleapis.com/maps/api/geocode/json'
>>> latitudes = []
>>> longitudes = []
>>> for address in df[1].tolist():
... payload = {'address': address}
... r = requests.get(base_url, params=payload)
... coords = r.json()['results'][0]['geometry']['location']
... latitudes.append(coords['lat'])
... longitudes.append(coords['lng'])
...
>>> latitudes
[43.6572571, 43.6535161, 43.6472168, 43.6650199, 43.6617416]
>>> longitudes
[-79.37609119999999, -79.3688681, -79.39527749999999, -79.3851912, -79.369494]
Now put the results into the dataframe and display the complete result.
>>> df['latitudes'] = latitudes
>>> df['longitudes'] = longitudes
>>> df
0 \
0 The Animal Clinic
1 Sherbourne Animal Hospital
2 Spadina Animal Hospital
3 Wellesley Animal Hospital
4 Cabbagetown Pet Clinic
1 lat latitudes \
0 106 Mutual St Toronto Ontario M5B 2R7 -31 43.657257
1 320 Richmond Street East Unit 8 Toronto Onta... -42 43.653516
2 125 Spadina Avenue Toronto Ontario M5V 2K8 -20 43.647217
3 8 Wellesley St W Toronto Ontario M4Y 1E7 19 43.665020
4 239 Gerrard St E Toronto Ontario M5A 2G1 50 43.661742
longitudes
0 -79.376091
1 -79.368868
2 -79.395277
3 -79.385191
4 -79.369494