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Comparing Similar Data Frames with Like-Columns in Python

I'd like to compare the difference in data frames. xyz has all of the same columns as abc, but it has an additional column.
In the comparison, I'd like match up the two like columns (Sport) but only show the SportLeague in the output (if a difference exists, that is). Example, instead of showing 'Soccer' as a difference, show 'Soccer:MLS', which is the adjacent column in xyz)
Here's a screenshot of the two data frames:
import pandas as pd
import numpy as np
abc = {'Sport' : ['Football', 'Basketball', 'Baseball', 'Hockey'], 'Year' : ['2021','2021','2022','2022'], 'ID' : ['1','2','3','4']}
abc = pd.DataFrame({k: pd.Series(v) for k, v in abc.items()})
abc
xyz = {'Sport' : ['Football', 'Football', 'Basketball', 'Baseball', 'Hockey', 'Soccer'], 'SportLeague' : ['Football:NFL', 'Football:XFL', 'Basketball:NBA', 'Baseball:MLB', 'Hockey:NHL', 'Soccer:MLS'], 'Year' : ['2022','2019', '2022','2022','2022', '2022'], 'ID' : ['2','0', '3','2','4', '1']}
xyz = pd.DataFrame({k: pd.Series(v) for k, v in xyz.items()})
xyz = xyz.sort_values(by = ['ID'], ascending = True)
xyz
Code already tried:
abc.compare(xyz, align_axis=1, keep_shape=False, keep_equal=False)
The error I get is the following (since the data frames don't have the exact same columns):
Example. If xyz['Sport'] does not show up anywhere within abc['Sport'], then show xyz['SportLeague]' as the difference between the data frames
Further clarification of the logic:
Does abc['Sport'] appear anywhere in xyz['Sport']? If not, indicate "Not Found in xyz data frame". If it does exist, are its corresponding abc['Year'] and abc['ID'] values the same? If not, show "Change from xyz['Year'] and xyz['ID'] to abc['Year'] and abc['ID'].
Does xyz['Sport'] appear anywhere in abc['Sport']? If not, indicate "Remove xyz['SportLeague']".
What I've explained above is similar to the .compare method. However, the data frames in this example may not be the same length and have different amounts of variables.

If I understand you correctly, we basically want to merge both DataFrames, and then apply a number of comparisons between both DataFrames, and add a column that explains the course of action to be taken, given a certain result of a given comparison.
Note: in the example here I have added one sport ('Cricket') to your df abc, to trigger the condition abc['Sport'] does not exist in xyz['Sport'].
abc = {'Sport' : ['Football', 'Basketball', 'Baseball', 'Hockey','Cricket'], 'Year' : ['2021','2021','2022','2022','2022'], 'ID' : ['1','2','3','4','5']}
abc = pd.DataFrame({k: pd.Series(v) for k, v in abc.items()})
print(abc)
Sport Year ID
0 Football 2021 1
1 Basketball 2021 2
2 Baseball 2022 3
3 Hockey 2022 4
4 Cricket 2022 5
I've left xyz unaltered. Now, let's merge these two dfs:
df = xyz.merge(abc, on='Sport', how='outer', suffixes=('_xyz','_abc'))
print(df)
Sport SportLeague Year_xyz ID_xyz Year_abc ID_abc
0 Football Football:XFL 2019 0 2021 1
1 Football Football:NFL 2022 2 2021 1
2 Soccer Soccer:MLS 2022 1 NaN NaN
3 Baseball Baseball:MLB 2022 2 2022 3
4 Basketball Basketball:NBA 2022 3 2021 2
5 Hockey Hockey:NHL 2022 4 2022 4
6 Cricket NaN NaN NaN 2022 5
Now, we have a df where we can evaluate your set of conditions using np.select(conditions, choices, default). Like this:
conditions = [ df.Year_abc.isnull(),
df.Year_xyz.isnull(),
(df.Year_xyz != df.Year_abc) & (df.ID_xyz != df.ID_abc),
df.Year_xyz != df.Year_abc,
df.ID_xyz != df.ID_abc
]
choices = [ 'Sport not in abc',
'Sport not in xyz',
'Change year and ID to xyz',
'Change year to xyz',
'Change ID to xyz']
df['action'] = np.select(conditions, choices, default=np.nan)
Result as below with a new column action with notes on which course of action to take.
Sport SportLeague Year_xyz ID_xyz Year_abc ID_abc \
0 Football Football:XFL 2019 0 2021 1
1 Football Football:NFL 2022 2 2021 1
2 Soccer Soccer:MLS 2022 1 NaN NaN
3 Baseball Baseball:MLB 2022 2 2022 3
4 Basketball Basketball:NBA 2022 3 2021 2
5 Hockey Hockey:NHL 2022 4 2022 4
6 Cricket NaN NaN NaN 2022 5
action
0 Change year and ID to xyz # match, but mismatch year and ID
1 Change year and ID to xyz # match, but mismatch year and ID
2 Sport not in abc # no match: Sport in xyz, but not in abc
3 Change ID to xyz # match, but mismatch ID
4 Change year and ID to xyz # match, but mismatch year and ID
5 nan # complete match: no action needed
6 Sport not in xyz # no match: Sport in abc, but not in xyz
Let me know if this is a correct interpretation of what you are looking to achieve.

How to iterate over columns and check condition by group

I have data for many countries over a period of time (2001-2003). It looks something like this:
index
year
country
inflation
GDP
1
2001
AFG
nan
48
2
2002
AFG
nan
49
3
2003
AFG
nan
50
4
2001
CHI
3.0
nan
5
2002
CHI
5.0
nan
6
2003
CHI
7.0
nan
7
2001
USA
nan
220
8
2002
USA
4.0
250
9
2003
USA
2.5
280
I want to drop countries in case there is no data (i.e. values are missing for all years) for any given variable.
In the example table above, I want to drop AFG (because it misses all values for inflation) and CHI (GDP missing). I don't want to drop observation #7 just because one year is missing.
What's the best way to do that?

This should work by filtering all values that have nan in one of (inflation, GDP):
(
df.groupby(['country'])
.filter(lambda x: not x['inflation'].isnull().all() and not x['GDP'].isnull().all())
)
Note, if you have more than two columns you can work on a more general version of this:
df.groupby(['country']).filter(lambda x: not x.isnull().all().any())
If you want this to work with a specific range of year instead of all columns, you can set up a mask and change the code a bit:
mask = (df['year'] >= 2002) & (df['year'] <= 2003) # mask of years
grp = df.groupby(['country']).filter(lambda x: not x[mask].isnull().all().any())

You can also try this:
# check where the sum is equal to 0 - means no values in the column for a specific country
group_by = df.groupby(['country']).agg({'inflation':sum, 'GDP':sum}).reset_index()
# extract only countries with information on both columns
indexes = group_by[ (group_by['GDP'] != 0) & ( group_by['inflation'] != 0) ].index
final_countries = list(group_by.loc[ group_by.index.isin(indexes), : ]['country'])
# keep the rows contains the countries
df = df.drop(df[~df.country.isin(final_countries)].index)

You could reshape the data frame from long to wide, drop nulls, and then convert back to wide.
To convert from long to wide, you can use pivot functions. See this question too.
Here's code for dropping nulls, after its reshaped:
df.dropna(axis=0, how= 'any', thresh=None, subset=None, inplace=True) # Delete rows, where any value is null
To convert back to long, you can use pd.melt.

argument of type "float" is not iterable when trying to use for loop

I have a countrydf as below, in which each cell in the country column contains a list of the countries where the movie was released.
countrydf
id Country release_year
s1 [US] 2020
s2 [South Africa] 2021
s3 NaN 2021
s4 NaN 2021
s5 [India] 2021
I want to make a new df which look like this:
country_yeardf
Year US UK Japan India
1925 NaN NaN NaN NaN
1926 NaN NaN NaN NaN
1927 NaN NaN NaN NaN
1928 NaN NaN NaN NaN
It has the release year and the number of movies released in each country.
My solution is that: with a blank df like the second one, run a for loop to count the number of movies released and then modify the value in the cell relatively.
countrylist=['Afghanistan', 'Aland Islands', 'Albania', 'Algeria', 'American Samoa', 'Andorra', 'Angola', 'Anguilla', 'Antarctica', ….]
for x in countrylist:
for j in list(range(0,8807)):
if x in countrydf.country[j]:
t=int (countrydf.release_year[j] )
country_yeardf.at[t, x] = country_yeardf.at[t, x]+1
an error occurred which read:
TypeError Traceback (most recent call last)
<ipython-input-25-225281f8759a> in <module>()
1 for x in countrylist:
2 for j in li:
----> 3 if x in countrydf.country[j]:
4 t=int(countrydf.release_year[j])
5 country_yeardf.at[t, x] = country_yeardf.at[t, x]+1
TypeError: argument of type 'float' is not iterable
I don’t know which one is of float type here, I have check the type of countrydf.country[j] and it returned int.
I was using pandas and I am just getting started with it. Can anyone please explain the error and suggest a solution for a df that I want to create?
P/s: my English is not so good so hop you guys understand.

Here is a solution using groupby
df = pd.DataFrame([['US', 2015], ['India', 2015], ['US', 2015], ['Russia', 2016]], columns=['country', 'year'])
country year
0 US 2015
1 India 2015
2 US 2015
3 Russia 2016
Now just groupby country and year and unstack the output:
df.groupby(['year', 'country']).size().unstack()
country India Russia US
year
2015 1.0 NaN 2.0
2016 NaN 1.0 NaN

Some alternative ways to achieve this in pandas without loops.
If the Country Column have more than 1 value in the list in each row, you can try the below:
>>df['Country'].str.join("|").str.get_dummies().groupby(df['release_year']).sum()
India South Africa US
release_year
2020 0 0 1
2021 1 1 0
Else if Country has just 1 value per row in the list as you have shown in the example, you can use crosstab
>>pd.crosstab(df['release_year'],df['Country'].str[0])
Country India South Africa US
release_year
2020 0 0 1
2021 1 1 0

pandas groupby replace based on condition

I have a dataset structures as below:
index country city Data
0 AU Sydney 23
1 AU Sydney 45
2 AU Unknown 2
3 CA Toronto 56
4 CA Toronto 2
5 CA Ottawa 1
6 CA Unknown 2
I want to replace 'Unknown' in the city column with the mode of the occurences of cities per country. The result would be:
...
2 AU Sydney 2
...
6 CA Toronto 2
I can get the city modes with:
city_modes = df.groupby('country')['city'].apply(lambda x: x.mode().iloc[0])
And I can replace values with:
df['column']=df.column.replace('Unknown', 'something')
But i cant work out how to combine these to only replace unknowns for each country based on mode of occurrence of cities.
Any ideas?

Use transform for Series with same size as original DataFrame and set new values by numpy.where:
city_modes = df.groupby('country')['city'].transform(lambda x: x.mode().iloc[0])
df['column'] = np.where(df['column'] == 'Unknown',city_modes, df['column'])
Or:
df.loc[df['column'] == 'Unknown', 'column'] = city_modes

Filtering Dataframe in Python

I have a dataframe with 2 columns as below:
Index Year Country
0 2015 US
1 2015 US
2 2015 UK
3 2015 Indonesia
4 2015 US
5 2016 India
6 2016 India
7 2016 UK
I want to create a new dataframe containing the maximum count of country in every year.
The new dataframe will contain 3 columns as below:
Index Year Country Count
0 2015 US 3
1 2016 India 2
Is there any function in pandas where this can be done quickly?

One way can be to use groupby and along with size for finding in each category adn sort values and slice by possible number of year. You can try the following:
num_year = df['Year'].nunique()
new_df = df.groupby(['Year', 'Country']).size().rename('Count').sort_values(ascending=False).reset_index()[:num_year]
Result:
Year Country Count
0 2015 US 3
1 2016 India 2

Use:
1.
First get count of each pairs Year and Country by groupby and size.
Then get index of max value by idxmax and select row by loc:
df = df.groupby(['Year','Country']).size()
df = df.loc[df.groupby(level=0).idxmax()].reset_index(name='Count')
print (df)
Year Country Count
0 2015 US 3
1 2016 India 2
2.
Use custom function with value_counts and head:
df = df.groupby('Year')['Country']
.apply(lambda x: x.value_counts().head(1))
.rename_axis(('Year','Country'))
.reset_index(name='Count')
print (df)
Year Country Count
0 2015 US 3
1 2016 India 2

Just provide a method without groupby
Count=pd.Series(list(zip(df2.Year,df2.Country))).value_counts()
.head(2).reset_index(name='Count')
Count[['Year','Country']]=Count['index'].apply(pd.Series)
Count.drop('index',1)
Out[266]:
Count Year Country
0 3 2015 US
1 2 2016 India