pegboard: Explore and Manipulate Markdown Curricula from the Carpentries

title: "Looping Over Data Sets" teaching: 5 exercises: 10 questions: - "How can I process many data sets with a single command?" objectives: - "Be able to read and write globbing expressions that match sets of files." - "Use glob to create lists of files." - "Write for loops to perform operations on files given their names in a list." keypoints: - "Use a for loop to process files given a list of their names." - "Use glob.glob to find sets of files whose names match a pattern." - "Use glob and for to process batches of files."

Use a `for` loop to process files given a list of their names.

A filename is a character string.
And lists can contain character strings.

import pandas as pd
for filename in ['data/gapminder_gdp_africa.csv', 'data/gapminder_gdp_asia.csv']:
    data = pd.read_csv(filename, index_col='country')
    print(filename, data.min())

{: .language-python}

data/gapminder_gdp_africa.csv gdpPercap_1952    298.846212
gdpPercap_1957    335.997115
gdpPercap_1962    355.203227
gdpPercap_1967    412.977514
⋮ ⋮ ⋮
gdpPercap_1997    312.188423
gdpPercap_2002    241.165877
gdpPercap_2007    277.551859
dtype: float64
data/gapminder_gdp_asia.csv gdpPercap_1952    331
gdpPercap_1957    350
gdpPercap_1962    388
gdpPercap_1967    349
⋮ ⋮ ⋮
gdpPercap_1997    415
gdpPercap_2002    611
gdpPercap_2007    944
dtype: float64

{: .output}

In Unix, the term "globbing" means "matching a set of files with a pattern".
The most common patterns are:
- * meaning "match zero or more characters"
- ? meaning "match exactly one character"
Python contains the glob library to provide pattern matching functionality
The glob library contains a function also called glob to match file patterns
E.g., glob.glob('*.txt') matches all files in the current directory whose names end with .txt.
Result is a (possibly empty) list of character strings.

import glob
print('all csv files in data directory:', glob.glob('data/*.csv'))

{: .language-python}

all csv files in data directory: ['data/gapminder_all.csv', 'data/gapminder_gdp_africa.csv', \
'data/gapminder_gdp_americas.csv', 'data/gapminder_gdp_asia.csv', 'data/gapminder_gdp_europe.csv', \
'data/gapminder_gdp_oceania.csv']

{: .output}

print('all PDB files:', glob.glob('*.pdb'))

{: .language-python}

all PDB files: []

{: .output}

Use `glob` and `for` to process batches of files.

Helps a lot if the files are named and stored systematically and consistently so that simple patterns will find the right data.

for filename in glob.glob('data/gapminder_*.csv'):
    data = pd.read_csv(filename)
    print(filename, data['gdpPercap_1952'].min())

{: .language-python}

data/gapminder_all.csv 298.8462121
data/gapminder_gdp_africa.csv 298.8462121
data/gapminder_gdp_americas.csv 1397.717137
data/gapminder_gdp_asia.csv 331.0
data/gapminder_gdp_europe.csv 973.5331948
data/gapminder_gdp_oceania.csv 10039.59564

{: .output}

This includes all data, as well as per-region data.
Use a more specific pattern in the exercises to exclude the whole data set.
But note that the minimum of the entire data set is also the minimum of one of the data sets, which is a nice check on correctness.

Determining Matches

Which of these files is not matched by the expression glob.glob('data/*as*.csv')?

data/gapminder_gdp_africa.csv

data/gapminder_gdp_americas.csv

data/gapminder_gdp_asia.csv

1 and 2 are not matched.

Solution

1 is not matched by the glob. {: .solution} {: .challenge}

Minimum File Size

Modify this program so that it prints the number of records in the file that has the fewest records.

~~~ import glob import pandas as pd fewest = _ for filename in glob.glob('data/*.csv'): dataframe = pd._(filename) fewest = min(____, dataframe.shape[0]) print('smallest file has', fewest, 'records') ~~~ {: .language-python} Note that the shape method returns a tuple with the number of rows and columns of the data frame.

Solution

~~~ import glob import pandas as pd fewest = float('Inf') for filename in glob.glob('data/*.csv'): dataframe = pd.read_csv(filename) fewest = min(fewest, dataframe.shape[0]) print('smallest file has', fewest, 'records') ~~~ {: .language-python} {: .solution} {: .challenge}

Comparing Data

Write a program that reads in the regional data sets and plots the average GDP per capita for each region over time in a single chart.

Solution

This solution builds a useful legend by using the string split method to extract the region from the path 'data/gapminder_gdp_a_specific_region.csv'. The [pathlib module] also provides useful abstractions for file and path manipulation like returning the name of a file without the file extension. ~~~ import glob import pandas as pd import matplotlib.pyplot as plt fig, ax = plt.subplots(1,1) for filename in glob.glob('data/gapminder_gdp*.csv'): dataframe = pd.read_csv(filename) # extract from the filename, expected to be in the format 'data/gapminder_gdp_.csv'. # we will split the string using the split method and _ as our separator, # retrieve the last string in the list that split returns (<region>.csv), # and then remove the .csv extension from that string. region = filename.split('_')[-1][:-4] dataframe.mean().plot(ax=ax, label=region) plt.legend() plt.show() ~~~ {: .language-python} {: .solution} {: .challenge}