Looping Over Data Sets

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.

Learning 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.

Key Questions

How can I process many data sets with a single command?

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())

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

Use `glob.glob` to find sets of files whose names match a pattern

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’s standard library contains the glob module to provide pattern matching functionality
The glob module 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'))

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']

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

all PDB files: []

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())

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

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.

Exercise: 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

Solution

1 is not matched by the glob.

Exercise: 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')

Note that the DataFrame.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')

You might have chosen to initialize the fewest variable with a number greater than the numbers you’re dealing with, but that could lead to trouble if you reuse the code with bigger numbers. Python permits you to use positive infinity, which will work no matter how big your numbers are. What other special strings does the float function recognize?

Exercise: 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. Pandas will raise an error if it encounters non-numeric columns in a dataframe computation so you may need to either filter out those columns or tell pandas to ignore them.

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’.

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 <region> from the filename, expected to be in the format 'data/gapminder_gdp_<region>.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.
    # NOTE: the pathlib module covered in the next callout also offers
    # convenient abstractions for working with filesystem paths and could solve this as well:
    # from pathlib import Path
    # region = Path(filename).stem.split('_')[-1]
    region = filename.split('_')[-1][:-4]
    # extract the years from the columns of the dataframe
    headings = dataframe.columns[1:]
    years = headings.str.split('_').str.get(1)
    # pandas raises errors when it encounters non-numeric columns in a dataframe computation
    # but we can tell pandas to ignore them with the `numeric_only` parameter
    dataframe.mean(numeric_only=True).plot(ax=ax, label=region)
    # NOTE: another way of doing this selects just the columns with gdp in their name using the filter method
    # dataframe.filter(like="gdp").mean().plot(ax=ax, label=region)
# set the title and labels
ax.set_title('GDP Per Capita for Regions Over Time')
ax.set_xticks(range(len(years)))
ax.set_xticklabels(years)
ax.set_xlabel('Year')
plt.tight_layout()
plt.legend()
plt.show()

Dealing with File Paths

The pathlib module provides useful abstractions for file and path manipulation like returning the name of a file without the file extension. This is very useful when looping over files and directories. In the example below, we create a Path object and inspect its attributes.

from pathlib import Path

p = Path("data/gapminder_gdp_africa.csv")
print(p.parent)
print(p.stem)
print(p.suffix)

data
gapminder_gdp_africa
.csv

Hint: Check all available attributes and methods on the Path object with the dir() function.

Key Points

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

Use glob.glob to find sets of files whose names match a pattern

Use glob and for to process batches of files

Solution

Solution

Solution

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