I have a dataframe which has a column called regional_codes. Now I need to add a new column into the dataframe where the regional codes are replaced by the list of countries that are attributed to that region.
For eg. if the regional_codes contains ['asia'] then I need my new column to have the list of asian countries like ['china','japan','india','bangaldesh'...]
Currently what I do is that I have created a separate list for each region and I use something like this code
asia_list= ['asia','china','japan','india'...]
output_list = []
output_list+= [asia_list for w in regional_codes if w in asia_list]
output_list+= [africa_list for w in regional_codes if w in africa_list]
and so on until all the regional lists are exhausted
With the codes that I have provided above, my results are exactly what I need and it is efficient in terms of running time as well. However, I feel like I am doing it in a very long way. Therefore, I am looking for some suggestions that can help me shorten my code.
One way I found to do this is to create a DataFrame with all the needed data for your regional_codes and the regional_lists
import pandas as pd
import itertools
import numpy as np
# DF is your dataframe
# df is the dataframe containing the association between the regional_code and regional lists
df = pd.DataFrame({'regional_code': ['asia', 'africa', 'europe'], 'ragional_list': [['China', 'Japan'], ['Morocco', 'Nigeria', 'Ghana'], ['France', 'UK', 'Germany', 'Spain']]})
# regional_code ragional_list
# 0 asia [China, Japan]
# 1 africa [Morocco, Nigeria, Ghana]
# 2 europe [France, UK, Germany, Spain]
df2 = pd.DataFrame({'regional_code': [['asia', 'africa'],['africa', 'europe']], 'ragional_list': [1,2]})
# regional_code ragional_list
# 0 [asia, africa] 1
# 1 [africa, europe] 2
df2['list'] = df2.apply(lambda x: list(itertools.chain.from_iterable((df.loc[df['regional_code']==i, 'ragional_list'] for i in x.loc['regional_code']))), axis=1)
# In [95]: df2
# Out[95]:
# regional_code ragional_list list
# 0 [asia, africa] 1 [[China, Japan], [Morocco, Nigeria, Ghana]]
# 1 [africa, europe] 2 [[Morocco, Nigeria, Ghana], [France, UK, Germa...
Now we flatten the df2['list']
df2['list'] = df2['list'].apply(np.concatenate)
# regional_code ragional_list list
# 0 [asia, africa] 1 [China, Japan, Morocco, Nigeria, Ghana]
# 1 [africa, europe] 2 [Morocco, Nigeria, Ghana, France, UK, Germany,...
I guess this answers your question?
Related
I am having trouble with pandas replace-function. Let's say we have an example dataframe like this:
df = pd.DataFrame({'State': ['Georgia', 'Alabama', 'Tennessee'],
'Cities': [['Atlanta', 'Albany'], ['Montgomery', 'Huntsville', 'Birmingham'], ['Nashville', 'Knoxville']]})
>>> df
State Cities
0 Georgia [Atlanta, Albany]
1 Alabama [Montgomery, Huntsville, Birmingham]
2 Tennessee [Nashville, Knoxville]
Now I want to replace the state names and city names all by abbreviations. I have two dictionaries that define the replacement values:
state_abbrv = {'Alabama': 'AL', 'Georgia': 'GA', 'Tennessee': 'TN'}
city_abbrv = {'Albany': 'Alb.', 'Atlanta': 'Atl.', 'Birmingham': 'Birm.',
'Huntsville': 'Htsv.', 'Knoxville': 'Kxv.',
'Montgomery': 'Mont.', 'Nashville': 'Nhv.'}
When using pd.DataFrame.replace() on the "States" column (which only contains one value per row) it works as expected and replaces all state names:
>>> df.replace({'State': state_abbrv})
State Cities
0 GA [Atlanta, Albany]
1 AL [Montgomery, Huntsville, Birmingham]
2 TN [Nashville, Knoxville]
I was hoping that it would also individually replace all matching names within the lists of the "Cities" column, but unfortunately it does not seem to work as all cities remain unabbreviated:
>>> df.replace({'Cities': city_abbrv})
State Cities
0 Georgia [Atlanta, Albany]
1 Alabama [Montgomery, Huntsville, Birmingham]
2 Tennessee [Nashville, Knoxville]
How do I get the pd.DataFrame.replace() function to individually circle through all list elements in the column per row and replace accordingly?
Try:
explode to split the list into individual rows
replace each column using the relevant dictionary
groupby and agg to get back the original structure
>>> output = df.explode("Cities").replace({"State": state_abbrv, "Cities": city_abbrv}).groupby("State", as_index=False)["Cities"].agg(list)
State Cities
0 AL [Mont., Htsv., Birm.]
1 GA [Atl., Alb.]
2 TN [Nhv., Kxv.]
I would like to add the regional information to the main table that contains entity and account columns. In this way, each row in the main table should be duplicated, just like the append tool in Alteryx.
Is there a way to do this operation with Pandas in Python?
Thanks!
Unfortunately no build-in method exist, as you'll need to build cartesian product of those DataFrame check that fancy explanation of merge DataFrames in pandas
But for your specific problem, try this:
import pandas as pd
import numpy as np
df1 = pd.DataFrame(columns=['Entity', 'Account'])
df1.Entity = ['Entity1', 'Entity1']
df1.Account = ['Sales', 'Cost']
df2 = pd.DataFrame(columns=['Region'])
df2.Region = ['North America', 'Europa', 'Asia']
def cartesian_product_simplified(left, right):
la, lb = len(left), len(right)
ia2, ib2 = np.broadcast_arrays(*np.ogrid[:la,:lb])
return pd.DataFrame(
np.column_stack([left.values[ia2.ravel()], right.values[ib2.ravel()]]))
resultdf = cartesian_product_simplified(df1, df2)
print(resultdf)
output:
0 1 2
0 Entity1 Sales North America
1 Entity1 Sales Europa
2 Entity1 Sales Asia
3 Entity1 Cost North America
4 Entity1 Cost Europa
5 Entity1 Cost Asia
as expected.
Btw, please provide the Data Frame the next time as code, not as a screenshot or even as link. It helps up saving time (please check how to ask)
I have a dataframe, where I have set indexes as countries. However there are also groups of countries such as Sub-Saharan Africa (IDA & IBRD countries) or Middle East & North Africa (IDA & IBRD countries). I want to delete them. I want just countries to stay.
Example imput dataframe where indexes are:
Antigua and Barbuda
Angola
Arab World
Wanted output dataframe:
Antigua and Barbuda
Angola
My idea was using pycountry, however it does nothing.
countr=list(pycountry.countries)
for idx in df.index:
if idx in countr :
continue
else:
df.drop(index=idx)
Check your list of country names:
countries_list = []
for country in pycountry.countries:
countries_list.append(country.name)
Checking output:
>>> print(countries_list[0:5])
['Aruba', 'Afghanistan', 'Angola', 'Anguilla', 'Åland Islands']
You can do this if you want to get a dataframe of countries that are in your countries list:
import pycountry
import pandas as pd
country = {'Country Name': ['Antigua and Barbuda','USSR', 'United States', 'Germany','The Moon']}
df = pd.DataFrame(data=country)
countries_list = []
for country in pycountry.countries:
countries_list.append(country.name)
new_df = []
for i in df['Country Name']:
if i in countries_list:
new_df.append(i)
Checking output:
>>> print(new_df)
['Antigua and Barbuda', 'United States', 'Germany']
Otherwise, for your specific code, try this:
Assuming you have your data in a dataframe 'df':
import pandas as pd
import pycountry
countries_list = []
for country in pycountry.countries:
countries_list.append(country.name)
for idx in df.index:
if idx in countries_list:
continue
else:
df.drop(index=idx)
Let me know if this works for you.
I have a dataframe as:-
Filtered_data
['defence possessed russia china','factors driving china modernise']
['force bolster pentagon','strike capabilities pentagon congress detailing china']
[missiles warheads', 'deterrent face continued advances']
......
......
I just want to split each list elements into sub-elements(tokenized words).So, output Im looking for as:-
Filtered_data
[defence, possessed,russia,factors,driving,china,modernise]
[force,bolster,strike,capabilities,pentagon,congress,detailing,china]
[missiles,warheads, deterrent,face,continued,advances]
here is my code what I have tried
for text in df['Filtered_data'].iteritems():
for i in text.split():
print (i)
Use list comprehension with split and flatenning:
df['Filtered_data'] = df['Filtered_data'].apply(lambda x: [z for y in x for z in y.split()])
print (df)
Filtered_data
0 [defence, possessed, russia, china, factors, d...
1 [force, bolster, pentagon, strike, capabilitie...
2 [missiles, warheads, deterrent, face, continue...
EDIT:
For unique values is standard way use sets:
df['Filtered_data'] = df['Filtered_data'].apply(lambda x: list(set([z for y in x for z in y.split()])))
print (df)
Filtered_data
0 [russia, factors, defence, driving, china, mod...
1 [capabilities, detailing, china, force, pentag...
2 [deterrent, advances, face, warheads, missiles...
But if ordering of values is important use pandas.unique:
df['Filtered_data'] = df['Filtered_data'].apply(lambda x: pd.unique([z for y in x for z in y.split()]).tolist())
print (df)
Filtered_data
0 [defence, possessed, russia, china, factors, d...
1 [force, bolster, pentagon, strike, capabilitie...
2 [missiles, warheads, deterrent, face, continue...
You can use itertools.chain + toolz.unique. The benefit of toolz.unique versus set is it preserves ordering.
from itertools import chain
from toolz import unique
df = pd.DataFrame({'strings': [['defence possessed russia china','factors driving china modernise'],
['force bolster pentagon','strike capabilities pentagon congress detailing china'],
['missiles warheads', 'deterrent face continued advances']]})
df['words'] = df['strings'].apply(lambda x: list(unique(chain.from_iterable(i.split() for i in x))))
print(df.iloc[0]['words'])
['defence', 'possessed', 'russia', 'china', 'factors', 'driving', 'modernise']
I have two data frames: Disaster, CountryInfo
Disaster has a column country code which has some null values
for example:
Disaster:
1.**Country** - **Country_code**
2.India - Null
3.Afghanistan - AFD
4.India - IND
5.United States - Null
CountryInfo:
0.**CountryName** - **ISO**
1.India - IND
2.Afganistan - AFD
3.United States - US
I need to fill the country code with reference to the country name.Can anyone suggest a solution for this?
You can simply use map with a Series.
With this approach all values are overwritten not only non NaN.
# Test data
disaster = pd.DataFrame({'Country': ['India', 'Afghanistan', 'India', 'United States'],
'Country_code': [np.nan, 'AFD', 'IND', np.nan]})
country = pd.DataFrame({'Country': ['India', 'Afghanistan', 'United States'],
'Country_code': ['IND', 'AFD', 'US']})
# Transforming country into a Series in order to be able to map it directly
country_se = country.set_index('Country').loc[:, 'Country_code']
# Map
disaster['Country_code'] = disaster['Country'].map(country_se)
print(disaster)
# Country Country_code
# 0 India IND
# 1 Afghanistan AFD
# 2 India IND
# 3 United States US
You can filter on NaN if you do not want to overwrite all values.
disaster.loc[pd.isnull(disaster['Country_code']),
'Country_code'] = disaster['Country'].map(country_se)