Identifying poor communities using the U.S. Census API

Programming Python
1

Hi guys, nice to meet you and I hope this finds you all in good spirits.

Help would be appreciated : Exercise 3: Remove Missing Values and Correct Data Types

Before analyzing the data, you need to ensure your data is complete and correctly formatted.

:arrow_forward::arrow_forward::arrow_forward: Directions

  1. Remove all rows with missing values (code is given).
  2. Convert the data type of all numeric columns, which is all columns except "county_state", to "Int64".

If you are stuck, click here for extra hints!

To convert column types, you can iterate over all the column names you want to change, and use the .astype() method to convert the column data type to “Int64”.

[13]:
# remove rows with missing values.

census_df_clean = census_df.dropna().copy()

# convert columns to numeric.
# get all the numerical column (all except "county_state" in index 1)

numeric_columns = ["county", "employed_male", "employed_total",

"female_pop_over_75", "female_pop_under_5", "male_pop_over_75",

"male_pop_under_5", "population", "poverty_count",

"poverty_count_female_over_75", "poverty_count_female_under_5",

"poverty_count_male_over_75", "poverty_count_male_under_5", "state",

"total_pop_male"]


### START CODE HERE ###

# iterate over each of the columns and convert to numeric

for None in None:

census_df_clean[None] = None

### END CODE HERE ###

Cell In[13], line 18 for None in None: ^ SyntaxError: cannot assign to None

[14]:

# 🔒This cell is locked. You will not be able to edit it.

# print data types

print("\nData types:")

print(census_df_clean.dtypes)

Data types:

--------------------------------------------------------------------------- NameError Traceback (most recent call last) Cell In[14], line 5 1 # 🔒This cell is locked. You will not be able to edit it. 2 3 # print data types 4 print("\nData types:") ----> 5 print(census_df_clean.dtypes) NameError: name 'census_df_clean' is not defined

Expected output:
<small>

Thanks for any help

2

Do you mind editing your post and wrapping the multi-line code snippets and error messages in fenced code blocks?
Otherwise the Markdown parser here on the forum interprets some characters as formatting directives.

Where do you need help?
Do you understand the instructions?
Do you understand the error message?

3

Is this anything to do with Exercism? :slight_smile:

4

No. We (the Python track) don’t have any exercises that deal with US census data or data cleaning in this fashion.

This also appears to be cut-and-pasted from Jupyter. We don’t use Jupyter Notebooks for any of our exercises.

5

Since this is evidently some sort of homework, it’d be helpful to show us what you’ve tried so far and what went wrong. Otherwise, we’d just be giving you the answer, and that doesn’t help you in the long term.

1 Like
6

Guys, thanks for the lowdown on this. I am about to try another strategy and return to you soon(BTW total newbie here, so please forgive any lapse in programming protocol).

1 Like