Develop Reference

Multiply a specific value with a series of columns based on a Condition in Pandas Dataframe

I have data of a certain country that gives the certain age group population in a time series. I am trying to multiply the number of the female population with -1 to display it on the other side of the pyramid graph. I have achieved that for one year i.e 1960 (see code below). Now I want to achieve the same results for all the columns from 1960-2020
PakPopulation.loc[PakPopulation['Gender']=="Female",['1960']]=PakPopulation['1960'].apply(lambda x:-x)
I have also tried the following solution but no luck:
PakPopulation.loc[PakPopulation['Gender']=="Female",[:,['1960':'2019']]=PakPopulation[:,['1960':'2019']].apply(lambda x:-x)
Schema:
Country
Age Group
Gender
1960
1961
1962
XYZ
0-4
Male
5880k
5887k
6998k
XYZ
0-4
Female
5980k
6887k
7998k

You could build a list of years and use that list as part of your selection:
import pandas as pd
PakPopulation = pd.DataFrame({
'Country': {0: 'XYZ', 1: 'ABC'},
'Age Group': {0: '0-4', 1: '0-4'},
'Gender': {0: 'Male', 1: 'Female'},
'1960': {0: 5880, 1: 5980},
'1961': {0: 5887, 1: 6887},
'1962': {0: 6998, 1: 7998},
})
start_year = 1960
end_year = 1962
years_lst = list(map(str, range(start_year, end_year + 1)))
PakPopulation.loc[PakPopulation['Gender'] == "Female", years_lst] = \
PakPopulation[years_lst].apply(lambda x: -x)
print(PakPopulation)
Output:
Country Age Group Gender 1960 1961 1962
0 XYZ 0-4 Male 5880 5887 6998
1 ABC 0-4 Female -5980 -6887 -7998

Apply a function on elements in a Pandas column, grouped on another column

I have a dataset with several columns.
Now what I want is to basically calculate score based on a particular column ("name") but grouped on the "id" column.
_id fName lName age
0 ABCD Andrew Schulz
1 ABCD Andreww 23
2 DEFG John boy
3 DEFG Johnn boy 14
4 CDGH Bob TANNA 13
5 ABCD. Peter Parker 45
6 DEFGH Clark Kent 25
So what I am looking is whether for the same id, I am getting similar entries, so I can remove those entries based on a threshold score values. Like here if i run it for col "fName". I should be able to reduce this dataframe to based on a score threshold:
_id fName lName age
0 ABCD Andrew Schulz 23
2 DEFG John boy 14
4 CDGH Bob TANNA 13
5 ABCD Peter Parker 45
6 DEFG Clark Kent 25
I intend to use pyjarowinkler.
If I had two independent columns (without all the group by stuff) to check, this is how I use it.
df['score'] = [distance.get_jaro_distance(x, y) for x, y in zip(df['name_1'],df['name_2'])]
df = df[df['score'] > 0.87]
Can someone suggest a pythonic and fast way of doing this
UPDATE
So, I have tried using record linkage library for this. And I have ended up at a dataframe containing pair of indexes that are similar called 'matches'. Now I just want to basically combine the data.
# Indexation step
indexer = recordlinkage.Index()
indexer.block(left_on='_id')
candidate_links = indexer.index(df)
# Comparison step
compare_cl = recordlinkage.Compare()
compare_cl.string('fName', 'fName', method='jarowinkler', threshold=threshold, label='full_name')
features = compare_cl.compute(candidate_links, df)
# Classification step
matches = features[features.sum(axis=1) >= 1]
print(len(matches))
This is how matches looks:
index1 index2 fName
0 1 1.0
2 3 1.0
I need someone to suggest a way to combine the similar rows in a way that takes data from similar rows

just wanted to clear some doubts regarding your ques. Couldn't clear them in comments due to low reputation.
Like here if i run it for col "fName". I should be able to reduce this
dataframe to based on a score threshold:
So basically your function would return the DataFrame containing the first row in each group (by ID)? This will result in the above listed resultant DataFrame.
_id fName lName age
0 ABCD Andrew Schulz 23
2 DEFG John boy 14
4 CDGH Bob TANNA 13

I hope this code answer your question
r0 =['ABCD','Andrew','Schulz', '' ]
r1 =['ABCD','Andrew', '' , '23' ]
r2 =['DEFG','John' ,'boy' , '' ]
r3 =['DEFG','John' ,'boy' , '14' ]
r4 =['CDGH','Bob' ,'TANNA' , '13' ]
Rx =[r0,r1,r2,r3,r4]
print(Rx)
print()
Dict= dict()
for i in Rx:
if (Dict.__contains__(i[0]) == True):
if (i[2] != ''):
Dict[i[0]][2] = i[2]
if (i[3] != ''):
Dict[i[0]][3] = i[3]
else:
Dict[i[0]]=i
Rx[:] = Dict.values()
print(Rx)

I am lost with the 'score' part of your question, but if what you need is to fill the gaps in data with values from other rows and then drop the duplicates by id, maybe this can help:
df.replace('', np.nan, inplace=True)
df_filled = df.fillna(method='bfill').drop_duplicates('Id', keep='first')
First make sure that empty values are replaced with nulls. Then use fillna to 'back fill' the data. Then drop duplicates keeping the first occurrence of Id. fillna will fill the values from the next value found in the column, which may correspond to other Id, but since you will discard the duplicated rows, I believe drop_duplicates keeping the first occurrence will do the job. (This assumes that at least one value is provided in every column for every Id)
I've tested with this dataset and code:
data = [
['AABBCC', 'Andrew', '',],
['AABBCC', 'Andrew', 'Schulz'],
['AABBCC', 'Andrew', '', 23],
['AABBCC', 'Andrew', '',],
['AABBCC', 'Andrew', '',],
['DDEEFF', 'Karl', 'boy'],
['DDEEFF', 'Karl', ''],
['DDEEFF', 'Karl', '', 14],
['GGHHHH', 'John', 'TANNA', 13],
['HLHLHL', 'Bob', ''],
['HLHLHL', 'Bob', ''],
['HLHLHL', 'Bob', 'Blob'],
['HLHLHL', 'Bob', 'Blob', 15],
['HLHLHL', 'Bob','', 15],
['JLJLJL', 'Nick', 'Best', 20],
['JLJLJL', 'Nick', '']
]
df = pd.DataFrame(data, columns=['Id', 'fName', 'lName', 'Age'])
df.replace('', np.nan, inplace=True)
df_filled = df.fillna(method='bfill').drop_duplicates('Id', keep='first')
Output:
Id fName lName Age
0 AABBCC Andrew Schulz 23.0
5 DDEEFF Karl boy 14.0
8 GGHHHH John TANNA 13.0
9 HLHLHL Bob Blob 15.0
14 JLJLJL Nick Best 20.0
Hope this helps and apologies if I misunderstood the question.

Use a different dataframe to replace value of text in dataframe

I have a simple dataframe (df1) where I am replacing values with the replace function (see below). Instead of always having to change the names of the items I want to replace in the code, I would like this to be done from an excel sheet, where either the columns or rows give the different names that should be replaced. I would import the excel as a dataframe (df2). All I am missing is the scrip that would turn the info from df2 into the replace function.
df1 = pd.DataFrame({'Product':['Tart', 'Cookie', 'Black'],
'Quantity': [1234, 4, 333]})
print(df1)
Product Quantity
0 Tart 1234
1 Cookie 4
2 Black 333
This is what I used so far
sales = sales.replace (["Tart","Tart2", "Cookie", "Cookie2"], "Tartlet")
sales = sales.replace (["Ham and cheese Sandwich" , "Chicken focaccia"], "Sandwich")
After replacement
print(df1)
Product Quantity
0 Tartlet 1234
1 Tartlet 4
2 Black 333
This is how my dataframe 2 could look like (I am flexible how to design it) after I imported it from an excel file
df2 = pd.read_excel (setup_folder / "Product Replacements.xlsx", index_col= 0)
print (df2)
Tartlet Sandwich
0 Tart Ham and cheese Sandwich
1 Tart2 Chicken Focaccia
2 Cookie2 nan

Use:
df2 = pd.DataFrame({'Tartlet':['Tart', 'Tart2', 'Cookie'],
'Sandwich': ['Ham and Cheese Sandwich', 'Chicken Focaccia', 'another']})
#swap key values in dict
#http://stackoverflow.com/a/31674731/2901002
d1 = {k: oldk for oldk, oldv in df2.items() for k in oldv}
print (d1)
{'Tart': 'Tartlet', 'Tart2': 'Tartlet', 'Cookie': 'Tartlet', 'Ham and Cheese Sandwich':
'Sandwich', 'Chicken Focaccia': 'Sandwich', 'another': 'Sandwich'}
df1['Product'] = df1['Product'].replace(d1)
#for improve performance
#df1['Product'] = df1['Product'].map(d1).fillna(df1['Product'])
print (df1)
Product Quantity
0 Tartlet 1234
1 Tartlet 4
2 Black 333

pandas: Group by splitting string value in all rows (a column) and aggregation function

If i have dataset like this:
id person_name salary
0 [alexander, william, smith] 45000
1 [smith, robert, gates] 65000
2 [bob, alexander] 56000
3 [robert, william] 80000
4 [alexander, gates] 70000
If we sum that salary column then we will get 316000
I really want to know how much person who named 'alexander, smith, etc' (in distinct) makes in salary if we sum all of the salaries from its splitting name in this dataset (that contains same string value).
output:
group sum_salary
alexander 171000 #sum from id 0 + 2 + 4 (which contain 'alexander')
william 125000 #sum from id 0 + 3
smith 110000 #sum from id 0 + 1
robert 145000 #sum from id 1 + 3
gates 135000 #sum from id 1 + 4
bob 56000 #sum from id 2
as we see the sum of sum_salary columns is not the same as the initial dataset. all because the function requires double counting.
I thought it seems familiar like string count, but what makes me confuse is the way we use aggregation function. I've tried creating a new list of distinct value in person_name columns, then stuck comes.
Any help is appreciated, Thank you very much

Solutions working with lists in column person_name:
#if necessary
#df['person_name'] = df['person_name'].str.strip('[]').str.split(', ')
print (type(df.loc[0, 'person_name']))
<class 'list'>
First idea is use defaultdict for store sumed values in loop:
from collections import defaultdict
d = defaultdict(int)
for p, s in zip(df['person_name'], df['salary']):
for x in p:
d[x] += int(s)
print (d)
defaultdict(<class 'int'>, {'alexander': 171000,
'william': 125000,
'smith': 110000,
'robert': 145000,
'gates': 135000,
'bob': 56000})
And then:
df1 = pd.DataFrame({'group':list(d.keys()),
'sum_salary':list(d.values())})
print (df1)
group sum_salary
0 alexander 171000
1 william 125000
2 smith 110000
3 robert 145000
4 gates 135000
5 bob 56000
Another solution with repeating values by length of lists and aggregate sum:
from itertools import chain
df1 = pd.DataFrame({
'group' : list(chain.from_iterable(df['person_name'].tolist())),
'sum_salary' : df['salary'].values.repeat(df['person_name'].str.len())
})
df2 = df1.groupby('group', as_index=False, sort=False)['sum_salary'].sum()
print (df2)
group sum_salary
0 alexander 171000
1 william 125000
2 smith 110000
3 robert 145000
4 gates 135000
5 bob 56000

Another sol:
df_new=(pd.DataFrame({'person_name':np.concatenate(df.person_name.values),
'salary':df.salary.repeat(df.person_name.str.len())}))
print(df_new.groupby('person_name')['salary'].sum().reset_index())
person_name salary
0 alexander 171000
1 bob 56000
2 gates 135000
3 robert 145000
4 smith 110000
5 william 125000

Can be done concisely with dummies though performance will suffer due to all of the .str methods:
df.person_name.str.join('*').str.get_dummies('*').multiply(df.salary, 0).sum()
#alexander 171000
#bob 56000
#gates 135000
#robert 145000
#smith 110000
#william 125000
#dtype: int64

I parsed this as strings of lists, by copying OP's data and using pandas.read_clipboard(). In case this was indeed the case (a series of strings of lists), this solution would work:
df = df.merge(df.person_name.str.split(',', expand=True), left_index=True, right_index=True)
df = df[[0, 1, 2, 'salary']].melt(id_vars = 'salary').drop(columns='variable')
# Some cleaning up, then a simple groupby
df.value = df.value.str.replace('[', '')
df.value = df.value.str.replace(']', '')
df.value = df.value.str.replace(' ', '')
df.groupby('value')['salary'].sum()
Output:
value
alexander 171000
bob 56000
gates 135000
robert 145000
smith 110000
william 125000

Another way you can do this is with iterrows(). This will not be as fast jezraels solution. But it works:
ids = []
names = []
salarys = []
# Iterate over the rows and extract the names from the lists in person_name column
for ix, row in df.iterrows():
for name in row['person_name']:
ids.append(row['id'])
names.append(name)
salarys.append(row['salary'])
# Create a new 'unnested' dataframe
df_new = pd.DataFrame({'id':ids,
'names':names,
'salary':salarys})
# Groupby on person_name and get the sum
print(df_new.groupby('names').salary.sum().reset_index())
Output
names salary
0 alexander 171000
1 bob 56000
2 gates 135000
3 robert 145000
4 smith 110000
5 william 125000

List unique identifier of missing rows in a series

Is it possible to return the row number of missing values within a given series?
Name Age
Fred 25
John 38
Chris
I want to return the row number or some unique identifier of any rows where 'Age' is missing. i.e Chris

Use:
df = pd.DataFrame({'Age': [25.0, 38.0, np.nan]}, index=['Fred', 'John', 'Chris'])
print (df)
Age
Fred 25.0
John 38.0
Chris NaN
m = df['Age'].isnull()
print (df.index[m].tolist())
[Chris]

You can do df.index[pd.isnull(df['Age'])]