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Analyzing Data From Mutliple Files


Teaching: 20 min
Exercises: 10 min
  • How can I do the same operations on many different files?

  • 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.

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.

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

Use glob and for to process batches of files.

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

** 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')?

  1. data/gapminder_gdp_africa.csv
  2. data/gapminder_gdp_americas.csv
  3. data/gapminder_gdp_asia.csv


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.


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.


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.
    region = filename.split('_')[-1][:-4] 
    dataframe.mean().plot(ax=ax, label=region)

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)

Hint: It is possible to check all available attributes and methods on the Path object with the dir() 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.