Develop Reference

Reading nested json to pandas dataframe

I have below URL that has a JSON response. I need to read this json into a pandas dataframe and perform operations on top of it . This is a case of nested JSON which consists of multiple lists and dicts within dicts.
URL: 'http://api.nobelprize.org/v1/laureate.json'
I have tried below code:
import json, pandas as pd,requests
resp=requests.get('http://api.nobelprize.org/v1/laureate.json')
df=pd.json_normalize(json.loads(resp.content),record_path =['laureates'])
print(df.head(5))
Output-
id firstname surname born died \
0 1 Wilhelm Conrad Röntgen 1845-03-27 1923-02-10
1 2 Hendrik A. Lorentz 1853-07-18 1928-02-04
2 3 Pieter Zeeman 1865-05-25 1943-10-09
3 4 Henri Becquerel 1852-12-15 1908-08-25
4 5 Pierre Curie 1859-05-15 1906-04-19
bornCountry bornCountryCode bornCity \
0 Prussia (now Germany) DE Lennep (now Remscheid)
1 the Netherlands NL Arnhem
2 the Netherlands NL Zonnemaire
3 France FR Paris
4 France FR Paris
diedCountry diedCountryCode diedCity gender \
0 Germany DE Munich male
1 the Netherlands NL NaN male
2 the Netherlands NL Amsterdam male
3 France FR NaN male
4 France FR Paris male
prizes
0 [{'year': '1901', 'category': 'physics', 'shar...
1 [{'year': '1902', 'category': 'physics', 'shar...
2 [{'year': '1902', 'category': 'physics', 'shar...
3 [{'year': '1903', 'category': 'physics', 'shar...
4 [{'year': '1903', 'category': 'physics', 'shar...
But in this prizes comes as a list. If I create a separate dataframe for prizes, it has affiliations as list.I want all columns to come as separate columns. Some entires may/may not have prizes. So need to handle that case as well.
I went through this article https://towardsdatascience.com/all-pandas-json-normalize-you-should-know-for-flattening-json-13eae1dfb7dd. Looks like we'll have to use meta and error=ignore here, but not able to fix it. Appreciate your inputs here. Thanks.

You would have to do this in few steps.
The first step would be to extract the first record_path = ['laureates']
The second one would be record_path = ['laureates', 'prizes'] for the nested json records with meta path as the id from the parent record
Combine the two datasets by joining on the id column.
Drop the unnecessary columns and store
import json, pandas as pd, requests
resp = requests.get('http://api.nobelprize.org/v1/laureate.json')
df0 = pd.json_normalize(json.loads(resp.content),record_path = ['laureates'])
df1 = pd.json_normalize(json.loads(resp.content),record_path = ['laureates','prizes'], meta = [['laureates','id']])
output = pd.merge(df0, df1, left_on='id', right_on='laureates.id').drop(['prizes','laureates.id'], axis=1, inplace=False)
print('Shape of data ->',output.shape)
print('Columns ->',output.columns)
Shape of data -> (975, 18)
Columns -> Index(['id', 'firstname', 'surname', 'born', 'died', 'bornCountry',
'bornCountryCode', 'bornCity', 'diedCountry', 'diedCountryCode',
'diedCity', 'gender', 'year', 'category', 'share', 'motivation',
'affiliations', 'overallMotivation'],
dtype='object')

Found an alternate solution as well with lesser code. This works.
from flatten_json import flatten
data = winners['laureates']
dict_flattened = (flatten(record, '.') for record in data)
df = pd.DataFrame(dict_flattened)
print(df.shape)
(968, 43)

Sum columns by key values in another column

I have a pandas DataFrame like this:
city country city_population
0 New York USA 8300000
1 London UK 8900000
2 Paris France 2100000
3 Chicago USA 2700000
4 Manchester UK 510000
5 Marseille France 860000
I want to create a new column country_population by calculating a sum of every city for each country. I have tried:
df['Country population'] = df['city_population'].sum().where(df['country'])
But this won't work, could I have some advise on the problem?

Sounds like you're looking for groupby
import pandas as pd
data = {
'city': ['New York', 'London', 'Paris', 'Chicago', 'Manchester', 'Marseille'],
'country': ['USA', 'UK', 'France', 'USA', 'UK', 'France'],
'city_population': [8_300_000, 8_900_000, 2_100_000, 2_700_000, 510_000, 860_000]
}
df = pd.DataFrame.from_dict(data)
# group by country, access 'city_population' column, sum
pop = df.groupby('country')['city_population'].sum()
print(pop)
output:
country
France 2960000
UK 9410000
USA 11000000
Name: city_population, dtype: int64
Appending this Series to the DataFrame. (Arguably discouraged though, since it stores information redundantly and doesn't really fit the structure of the original DataFrame):
# add to existing df
pop.rename('country_population', inplace=True)
# how='left' to preserve original ordering of df
df = df.merge(pop, how='left', on='country')
print(df)
output:
city country city_population country_population
0 New York USA 8300000 11000000
1 London UK 8900000 9410000
2 Paris France 2100000 2960000
3 Chicago USA 2700000 11000000
4 Manchester UK 510000 9410000
5 Marseille France 860000 2960000

based on #Vaishali's comment, a one-liner
df['Country population'] = df.groupby([ 'country']).transform('sum')['city_population']

Pandas: How to find whether address in one dataframe is from city and state in another dataframe?

I have a dataframe of addresses as below:
main_df =
address
0 3, my_street, Mumbai, Maharashtra
1 Bangalore Karnataka 45th Avenue
2 TelanganaHyderabad some_street, some apartment
And I have a dataframe with city and state as below (note few states have cities with same names too:
city_state_df =
city state
0 Mumbai Maharashtra
1 Ahmednagar Maharashtra
2 Ahmednagar Bihar
3 Bangalore Karnataka
4 Hyderabad Telangana
I want to have a mapping of city and state next to each address. I am able to do so with iterrows() with nested for loops. However, both take more than an hour each for mere 15k records. What is the optimum way of achieving this considering addresses are randomly written and multiple states have same city name?
My code below:
main_df = pd.DataFrame({'address': ['3, my_street, Mumbai, Maharashtra', 'Bangalore Karnataka 45th Avenue', 'TelanganaHyderabad some_street, some apartment']})
city_state_df = pd.DataFrame({'city': ['Mumbai', 'Ahmednagar', 'Ahmednagar', 'Bangalore', 'Hyderabad'],
'state': ['Maharashtra', 'Maharashtra', 'Bihar', 'Karnataka', 'Telangana']})
df['city'] = np.nan
df['state'] = np.nan
for i, df_row in df.iterrows():
for j, city_row in city_state_df.iterrows():
if city_row['city'] in df_row['address']:
city_filtered = city[city['city'] == city_row['city']]
for k, fil_row in city_filtered.iterrows():
if fil_row['state'] in df_row['address']:
df_row['city'] = fil_row['city']
df_row['state'] = fil_row['state']
break
break

Hello maybe something like this:
main_df = main_df.reindex(columns=[*main_df.columns.tolist(), 'state', 'city'],fill_value=None)
for i, row in city_state_df.iterrows():
main_df.loc[(main_df.address.str.contains(row.city)) & \
(main_df.address.str.contains(row.state)), \
['city', 'state']] = [row.city, row.state]

Geopandas .merge() produces strange results

I have been trying to merge two geopandas dataframes based on a column and am getting some really weird results. To test this point I made two simple dataframes, and merged them:
import pandas as pd
import geopandas as gpd
df = pd.DataFrame(
{'City': ['Buenos Aires', 'Brasilia', 'Santiago', 'Bogota', 'Caracas'],
'Country': ['Argentina', 'Brazil', 'Chile', 'Colombia', 'Venezuela'],
'Latitude': [-34.58, -15.78, -33.45, 4.60, 10.48],
'Longitude': [-58.66, -47.91, -70.66, -74.08, -66.86]})
gdf = gpd.GeoDataFrame(
df, geometry=gpd.points_from_xy(df.Longitude, df.Latitude))
df2 = pd.DataFrame(
{'Capital': ['Buenos Aires', 'Brasilia', 'Santiago', 'Bogota',
'Caracas'],
'Abbreviation': ['ARG', 'BRA', 'CHI', 'COL', 'VZL']})
combined_df = gdf.merge(df2, left_on='City', right_on='Capital')
print(combined_df)
When I print the results, I get what I expected:
City Country ... Capital Abbreviation
0 Buenos Aires Argentina ... Buenos Aires ARG
1 Brasilia Brazil ... Brasilia BRA
2 Santiago Chile ... Santiago CHI
3 Bogota Colombia ... Bogota COL
4 Caracas Venezuela ... Caracas VZL
The two datasets are merged based on their common column, which is the 'city' column and the 'capital' column.
I have some other data I am working with. Here is a link to it
Both of the files are geopackages I've read in as geodataframes. Dataframe 1 has 16166 rows. Dataframe 2 has 15511 rows. They have a common ID column, 'ALTPARNO' AND 'altparno'. Here is the code I've tried to use to read them in and merge them:
import geopandas as gpd
dataframe1 = gpd.read_file(filepath, layer='allkeepers_2019')
dataframe2 = gpd.read_file(filepath, layer='keepers_2019')
results = dataframe1.merge(dataframe2, left_on='altparno', right_on='ALTPARNO')
When I look at my results, I have a dataframe with over 4 million rows (should be around 15,000).
What is going on?

Trying to use a list to populate a dataframe column

I have a dataframe (df) and I would like to create a new column called country, which is calculated buy looking at the region column and where the region value is present in the EnglandRegions list then the country value is set to England else its the value from the region column.
Please see below for my desired output:
name salary region B1salary country
0 Jason 42000 London 42000 England
1 Molly 52000 South West England
2 Tina 36000 East Midland England
3 Jake 24000 Wales Wales
4 Amy 73000 West Midlands England
You can see that all the values in country are set to England except for the value assigned to Jakes record that is set to Wales (as Wales is not in the EnglandRegions list). The code below produces the following error:
File "C:/Users/stacey/Documents/scripts/stacey.py", line 20
df['country'] = np.where((df.loc[df['region'].isin(EnglandRegions)),'England', df['region'])
^
SyntaxError: invalid syntax
The code is as follows:
import pandas as pd
import numpy as np
EnglandRegions = ["London", "South West", "East Midland", "West Midlands", "East Anglia"]
data = {'name': ['Jason', 'Molly', 'Tina', 'Jake', 'Amy'],
'salary': [42000, 52000, 36000, 24000, 73000],
'region': ['London', 'South West', 'East Midland', 'Wales', 'West Midlands']}
df = pd.DataFrame(data, columns = ['name', 'salary', 'region'])
df['B1salary'] = np.where((df['salary']>=40000) & (df['salary']<=50000) , df['salary'], '')
df['country'] = np.where((df.loc[df['region'].isin(EnglandRegions)),'England', df['region'])
print(df)

The specific issue the error is referencing is that you are missing a ] to enclose your .loc. However, fixing this won't work anyways. Try:
df['country'] = np.where(df['region'].isin(EnglandRegions), 'England', df['region'])
This is essentially what you already had in the line above it for B1salary anyways.