Looping Over Data Sets
Last updated on 2024-03-05 | Edit this page
Overview
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.
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.
PYTHON
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())OUTPUT
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: float64Use 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
globmodule to provide pattern matching functionality - The
globmodule contains a function also calledglobto 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.
OUTPUT
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
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.
PYTHON
for filename in glob.glob('data/gapminder_*.csv'):
data = pd.read_csv(filename)
print(filename, data['gdpPercap_1952'].min())OUTPUT
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.
1 is not matched by the glob.
Minimum File Size
Modify this program so that it prints the number of records in the file that has the fewest records.
PYTHON
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.
PYTHON
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
lets you use positive infinity, which will work no matter how big your
numbers are. What other special strings does the float
function recognize?
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.
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’.
PYTHON
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]
# 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)
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.
PYTHON
from pathlib import Path
p = Path("data/gapminder_gdp_africa.csv")
print(p.parent)
print(p.stem)
print(p.suffix)OUTPUT
data
gapminder_gdp_africa
.csvHint: Check all available attributes and methods on
the Path object with the dir() function.