Analyzing Data From Multiple Files
Overview
Teaching: 20 min
Exercises: 10 minQuestions
How can I do the same operations on many different files?
Objectives
Use a library function to get a list of filenames that match a wildcard pattern.
Write a
for
loop to process multiple 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())
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 calledglob
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.
** TODO add example here of using wildcard to generate a plot from multiple files **
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.
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')
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 theregion
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. 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) 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 aPath
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: It is possible to check all available attributes and methods on the
Path
object with thedir()
function!
Key Points
Use
glob.glob(pattern)
to create a list of files whose names match a pattern.Use
*
in a pattern to match zero or more characters, and?
to match any single character.