20230516053629-mode_python.org

Mode Python

Predefined Function

Create Predefined Function

return f"""
def the_strip_function(input_text:str, prefix:str):
    return prefix + " " + "*" + input_text.strip() + "*"
return the_strip_function({input_text}, {prefix})
"""

Get the Result of Predefined Function

example_input_text = "Lorem Ipsum"
example_prefix = "-"
<<strip_function("example_input_text", "example_prefix")>>

Pandas

Concat DataFrame

data = {
    'Name': ['Deden', 'Emily', 'Michael', 'Jessica', 'Brian'],
    'Age': [34, 30, 35, 35, 32],
    'Country': ['Indonesia', 'USA', 'UK', 'USA', 'Australia']
}
df = pd.DataFrame(data)
salary = {'Salary': [5000, 6000, 7000, 5500, 6500]}
df_additional = pd.DataFrame(salary)
df_combined = pd.concat([df, df_additional], axis=1)
<<pd2org("df_combined")>>

	Name	Age	Country	Salary
0	Deden	34	Indonesia	5000
1	Emily	30	USA	6000
2	Michael	35	UK	7000
3	Jessica	35	USA	5500
4	Brian	32	Australia	6500

Merge DataFrame

data = {
    'Name': ['Deden', 'Emily', 'Michael', 'Jessica', 'Brian'],
    'Age': [34, 30, 35, 35, 32],
    'Country': ['Indonesia', 'USA', 'UK', 'USA', 'Australia']
}
df = pd.DataFrame(data)
salary = {'Salary': [5000, 6000, 7000, 5500, 6500]}
df_additional = pd.DataFrame(salary)
df_combined = pd.concat([df, df_additional], axis=1)
<<pd2org("df_combined")>>

Print Dataframe to ORG Table

return f"return tabulate({df}, headers={df}.columns, tablefmt='orgtbl')"

data = pd.DataFrame([{"name": "Deden", "age": 34}, {"name": "Shinta", "age": 33}])
<<pd2org("data")>>

	name	age
0	Deden	34
1	Shinta	33

Grouping, Aggregating, and Sorting

data = {
    'Name': ['Deden', 'Emily', 'Michael', 'Jessica', 'Brian'],
    'Age': [34, 30, 35, 35, 32],
    'Country': ['Indonesia', 'USA', 'UK', 'USA', 'Australia']
}
df = pd.DataFrame(data)

df_grouped = df.groupby('Country').agg({'Age': 'mean', 'Name': 'count'}).reset_index()
df_sorted = df_grouped.sort_values(
    by=['Age'], ascending=False
).rename(columns={"Name": "Persons"}).reset_index(drop=True)
#+begin_quote
#+end_quote
<<pd2org("df_sorted")>>

	Country	Age	Persons
0	UK	35	1
1	Indonesia	34	1
2	USA	32.5	2
3	Australia	32	1

Applymap

data = {
    'Name': ['Deden', 'Emily', 'Michael', 'Jessica', 'Brian'],
    'Age': [34, 30, 35, 35, 32],
    'Country': ['Indonesia', 'USA', 'UK', 'USA', 'Australia'],
    'Favourite Food': [
        ['Rendang', 'Nasi Goreng'],
        ['Rendang'],
        ['Nasi Goreng','Nasi Goreng'],
        ['Rendang', 'Nasi Goreng'],
        ['Rendang', 'Nasi Goreng']
    ]
}
df = pd.DataFrame(data)

def transform_value(x):
    print(type(x))
    if isinstance(x, str):
        return x.upper()
    elif isinstance(x, int):
        return x + 1
    elif isinstance(x, list):
        # Sort, remove duplicate
        return ",".join(sorted(set(x)))
    else:
        return x

df_updated = df.applymap(transform_value)
<<pd2org("df_updated")>>

	Name	Age	Country	Favourite Food
0	DEDEN	35	INDONESIA	Nasi Goreng,Rendang
1	EMILY	31	USA	Rendang
2	MICHAEL	36	UK	Nasi Goreng
3	JESSICA	36	USA	Nasi Goreng,Rendang
4	BRIAN	33	AUSTRALIA	Nasi Goreng,Rendang

Fill Missing Value

df_missing = pd.DataFrame({
    'Name': ['Deden', 'Joy' ,'Emily', 'Michael', 'Jessica', 'Brian'],
    'Age': [34, None, 32, 31, None, 39],
    'Country': ['Indonesia', 'India', 'USA', 'UK', 'USA', None],
})

# Interpolate
df_filled = df_missing.fillna('N/A')
<<pd2org("df_filled")>>

	Name	Age	Country
0	Deden	34.0	Indonesia
1	Joy	N/A	India
2	Emily	32.0	USA
3	Michael	31.0	UK
4	Jessica	N/A	USA
5	Brian	39.0	N/A

Interpolate Missing Value

df_missing = pd.DataFrame({
    'Name': ['Deden', 'Joy' ,'Emily', 'Michael', 'Jessica', 'Brian'],
    'Age': [34, None, 32, 31, None, 39],
    'Country': ['Indonesia', 'India', 'USA', 'UK', 'USA', None],
})

# Interpolate
df_interpolated = df_missing.interpolate()
<<pd2org("df_interpolated")>>

	Name	Age	Country
0	Deden	34	Indonesia
1	Joy	33	India
2	Emily	32	USA
3	Michael	31	UK
4	Jessica	35	USA
5	Brian	39

Group-wise Transform

df = pd.DataFrame({
    'Name': ['Deden', 'Emily', 'Michael', 'Jessica', 'Brian'],
    'Age': [34, 30, 35, 35, 32],
    'Country': ['Indonesia', 'USA', 'UK', 'USA', 'Australia'],
})