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[Contents](../Contents.md) \| [Previous (1.5 Lists)](05_Lists.md) \| [Next (1.7 Functions)](07_Functions.md)
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# 1.6 File Management
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Most programs need to read input from somewhere. This section discusses file access.
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### File Input and Output
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Open a file.
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```python
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f = open('foo.txt', 'rt') # Open for reading (text)
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g = open('bar.txt', 'wt') # Open for writing (text)
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```
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Read all of the data.
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```python
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data = f.read()
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# Read only up to 'maxbytes' bytes
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data = f.read([maxbytes])
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```
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Write some text.
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```python
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g.write('some text')
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```
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Close when you are done.
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```python
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f.close()
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g.close()
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```
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Files should be properly closed and it's an easy step to forget.
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Thus, the preferred approach is to use the `with` statement like this.
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```python
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with open(filename, 'rt') as file:
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# Use the file `file`
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...
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# No need to close explicitly
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...statements
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```
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This automatically closes the file when control leaves the indented code block.
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### Common Idioms for Reading File Data
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Read an entire file all at once as a string.
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```python
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with open('foo.txt', 'rt') as file:
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data = file.read()
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# `data` is a string with all the text in `foo.txt`
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```
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Read a file line-by-line by iterating.
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```python
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with open(filename, 'rt') as file:
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for line in file:
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# Process the line
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```
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### Common Idioms for Writing to a File
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Write string data.
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```python
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with open('outfile', 'wt') as out:
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out.write('Hello World\n')
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...
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```
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Redirect the print function.
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```python
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with open('outfile', 'wt') as out:
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print('Hello World', file=out)
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...
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```
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## Exercises
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These exercises depend on a file `Data/portfolio.csv`. The file
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contains a list of lines with information on a portfolio of stocks.
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It is assumed that you are working in the `practical-python/Work/`
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directory. If you're not sure, you can find out where Python thinks
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it's running by doing this:
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```python
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>>> import os
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>>> os.getcwd()
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'/Users/beazley/Desktop/practical-python/Work' # Output vary
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>>>
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```
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### Exercise 1.26: File Preliminaries
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First, try reading the entire file all at once as a big string:
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```python
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>>> with open('Data/portfolio.csv', 'rt') as f:
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data = f.read()
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>>> data
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'name,shares,price\n"AA",100,32.20\n"IBM",50,91.10\n"CAT",150,83.44\n"MSFT",200,51.23\n"GE",95,40.37\n"MSFT",50,65.10\n"IBM",100,70.44\n'
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>>> print(data)
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name,shares,price
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"AA",100,32.20
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"IBM",50,91.10
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"CAT",150,83.44
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"MSFT",200,51.23
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"GE",95,40.37
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"MSFT",50,65.10
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"IBM",100,70.44
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>>>
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```
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In the above example, it should be noted that Python has two modes of
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output. In the first mode where you type `data` at the prompt, Python
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shows you the raw string representation including quotes and escape
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codes. When you type `print(data)`, you get the actual formatted
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output of the string.
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Although reading a file all at once is simple, it is often not the
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most appropriate way to do it—especially if the file happens to be
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huge or if contains lines of text that you want to handle one at a
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time.
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To read a file line-by-line, use a for-loop like this:
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```python
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>>> with open('Data/portfolio.csv', 'rt') as f:
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for line in f:
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print(line, end='')
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name,shares,price
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"AA",100,32.20
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"IBM",50,91.10
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...
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>>>
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```
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When you use this code as shown, lines are read until the end of the
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file is reached at which point the loop stops.
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On certain occasions, you might want to manually read or skip a
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*single* line of text (e.g., perhaps you want to skip the first line
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of column headers).
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```python
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>>> f = open('Data/portfolio.csv', 'rt')
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>>> headers = next(f)
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>>> headers
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'name,shares,price\n'
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>>> for line in f:
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print(line, end='')
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"AA",100,32.20
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"IBM",50,91.10
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...
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>>> f.close()
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>>>
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```
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`next()` returns the next line of text in the file. If you were to call it repeatedly, you would get successive lines.
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However, just so you know, the `for` loop already uses `next()` to obtain its data.
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Thus, you normally wouldn’t call it directly unless you’re trying to explicitly skip or read a single line as shown.
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Once you’re reading lines of a file, you can start to perform more processing such as splitting.
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For example, try this:
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```python
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>>> f = open('Data/portfolio.csv', 'rt')
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>>> headers = next(f).split(',')
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>>> headers
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['name', 'shares', 'price\n']
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>>> for line in f:
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row = line.split(',')
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print(row)
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['"AA"', '100', '32.20\n']
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['"IBM"', '50', '91.10\n']
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...
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>>> f.close()
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```
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*Note: In these examples, `f.close()` is being called explicitly because the `with` statement isn’t being used.*
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### Exercise 1.27: Reading a data file
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Now that you know how to read a file, let’s write a program to perform a simple calculation.
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The columns in `portfolio.csv` correspond to the stock name, number of
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shares, and purchase price of a single stock holding. Write a program called
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`pcost.py` that opens this file, reads all lines, and calculates how
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much it cost to purchase all of the shares in the portfolio.
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*Hint: to convert a string to an integer, use `int(s)`. To convert a string to a floating point, use `float(s)`.*
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Your program should print output such as the following:
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```bash
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Total cost 44671.15
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```
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### Exercise 1.28: Other kinds of "files"
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What if you wanted to read a non-text file such as a gzip-compressed
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datafile? The builtin `open()` function won’t help you here, but
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Python has a library module `gzip` that can read gzip compressed
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files.
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Try it:
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```python
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>>> import gzip
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>>> with gzip.open('Data/portfolio.csv.gz', 'rt') as f:
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for line in f:
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print(line, end='')
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... look at the output ...
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>>>
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```
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Note: Including the file mode of `'rt'` is critical here. If you forget that,
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you'll get byte strings instead of normal text strings.
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### Commentary: Shouldn't we being using Pandas for this?
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Data scientists are quick to point out that libraries like
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[Pandas](https://pandas.pydata.org) already have a function for
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reading CSV files. This is true--and it works pretty well.
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However, this is not a course on learning Pandas. Reading files
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is a more general problem than the specifics of CSV files.
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The main reason we're working with a CSV file is that it's a
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familiar format to most coders and it's relatively easy to work with
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directly--illustrating many Python features in the process.
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So, by all means use Pandas when you go back to work. For the
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rest of this course however, we're going to stick with standard
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Python functionality.
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[Contents](../Contents.md) \| [Previous (1.5 Lists)](05_Lists.md) \| [Next (1.7 Functions)](07_Functions.md)
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