CODEWITHSUNDEEP
pythonpandasdataframe
D45
D45

Reputation: 101

Creating a Function with multiple operations in Python

I am currently doing a project with baby name data. I am looking at the most popular male and female baby names in each decade starting with the 1950s. I am trying to create a function that will print out the top name for the data set that I input.

So far I have successfully created two datasets for each decade (one male and the other female)

This is the code that I have for the function but I can't seem to figure out how to make it work...

def getTopName(data):
    (data
        .drop(columns =['sex', 'prop'])
        .pivot(index = 'name', columns = 'year', values = 'n')
        .sum(axis=1) = data['decade']
        .sort_values(by = 'decade', ascending = False))
    print data[0:1]

Any suggestions on how to accomplish this?

My data looks like this: my data

Its currently in longform. Can i create a middle function that converts it to wide form and builds a new column where the totals from each year (1960, 1961, ... 1969) can be added together?

Upvotes: 3

Views: 1843

Answers (1)

benvdh
benvdh

Reputation: 603

Question 1 - Name with highest n per year

df.groupby(by='name', as_index=False)
      .count()
      .nlargest(1, 'number')
      .iloc[0]["name"]

Sample data

Question 2 - Transform data to wideform

Sample data on which this was tested

Pivot in pandas does not do aggregations. So I split up the steps in getting totals per year and totals per decade. Finally, I join those two to get the desired result:

import pandas as pd

df = pd.read_csv('set2.csv')

# add decade column
df["decade"] = df["year"] - (df["year"] % 10)

# add decade_title column to prevent join clashes
df["decade_total"] = df["decade"]
                       .apply(lambda decade_num: f"{str(decade_num)}_total")

# first pivot with n per year
per_year_df = df.pivot(index="name", columns="year", values="n")

# pivot cannot aggregate so we first aggregate and then pivot
per_decade_df = df\
    .groupby(by=["decade_total", "name"], as_index=False)\
    .agg({"n": 'sum'})\
    .pivot(index="name", columns="decade_total", values="n")

# finally we join the decade totals to the yearly counts
joined_df = per_year_df.join(per_decade_df)

Joined result table

Upvotes: 1

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