{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", " \"cognitiveclass.ai\n", "
\n", "\n", "# Reading Files Python\n", "\n", "Estimated time needed: **40** minutes\n", "\n", "## Objectives\n", "\n", "After completing this lab you will be able to:\n", "\n", "- Read text files using Python libraries\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "

Table of Contents

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\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "

Download Data

\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "--2020-10-22 02:25:27-- https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/PY0101EN/labs/example1.txt\n", "Resolving s3-api.us-geo.objectstorage.softlayer.net (s3-api.us-geo.objectstorage.softlayer.net)... 67.228.254.196\n", "Connecting to s3-api.us-geo.objectstorage.softlayer.net (s3-api.us-geo.objectstorage.softlayer.net)|67.228.254.196|:443... connected.\n", "HTTP request sent, awaiting response... 200 OK\n", "Length: 45 [text/plain]\n", "Saving to: ‘/resources/data/Example1.txt’\n", "\n", "/resources/data/Exa 100%[===================>] 45 --.-KB/s in 0s \n", "\n", "2020-10-22 02:25:27 (30.2 MB/s) - ‘/resources/data/Example1.txt’ saved [45/45]\n", "\n" ] } ], "source": [ "# Download Example file\n", "\n", "!wget -O /resources/data/Example1.txt https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/PY0101EN/labs/example1.txt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "

Reading Text Files

\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "One way to read or write a file in Python is to use the built-in open function. The open function provides a File object that contains the methods and attributes you need in order to read, save, and manipulate the file. In this notebook, we will only cover .txt files. The first parameter you need is the file path and the file name. An example is shown as follow:\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " The mode argument is optional and the default value is r. In this notebook we only cover two modes: \n", "\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For the next example, we will use the text file Example1.txt. The file is shown as follow:\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " We read the file: \n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# Read the Example1.txt\n", "\n", "example1 = \"/resources/data/Example1.txt\"\n", "file1 = open(example1, \"r\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " We can view the attributes of the file.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The name of the file:\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'/resources/data/Example1.txt'" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Print the path of file\n", "\n", "file1.name" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " The mode the file object is in:\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'r'" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Print the mode of file, either 'r' or 'w'\n", "\n", "file1.mode" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can read the file and assign it to a variable :\n" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'This is line 1 \\nThis is line 2\\nThis is line 3'" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Read the file\n", "\n", "FileContent = file1.read()\n", "FileContent" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The /n means that there is a new line. \n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can print the file: \n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "This is line 1 \n", "This is line 2\n", "This is line 3\n" ] } ], "source": [ "# Print the file with '\\n' as a new line\n", "\n", "print(FileContent)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The file is of type string:\n" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "str" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Type of file content\n", "\n", "type(FileContent)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " We must close the file object:\n" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "# Close file after finish\n", "\n", "file1.close()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "

A Better Way to Open a File

\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Using the with statement is better practice, it automatically closes the file even if the code encounters an exception. The code will run everything in the indent block then close the file object. \n" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "This is line 1 \n", "This is line 2\n", "This is line 3\n" ] } ], "source": [ "# Open file using with\n", "\n", "with open(example1, \"r\") as file1:\n", " FileContent = file1.read()\n", " print(FileContent)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The file object is closed, you can verify it by running the following cell: \n" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Verify if the file is closed\n", "\n", "file1.closed" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " We can see the info in the file:\n" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "This is line 1 \n", "This is line 2\n", "This is line 3\n" ] } ], "source": [ "# See the content of file\n", "\n", "print(FileContent)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The syntax is a little confusing as the file object is after the as statement. We also don’t explicitly close the file. Therefore we summarize the steps in a figure:\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We don’t have to read the entire file, for example, we can read the first 4 characters by entering three as a parameter to the method **.read()**:\n" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "This\n", " i\n" ] } ], "source": [ "# Read first four characters\n", "\n", "with open(example1, \"r\") as file1:\n", " print(file1.read(4))\n", " \n", " ##Experimenting:\n", " ##print(file1.read(2)) #if I run this instead of the below, will give the next two characters\n", " # print(file1.readlines(10)) #give output [' is line 1 \\n']\n", " # print(file1.readlines(10)) #gives output ['This is line 2\\n']\n", " #print(file1.readlines(1)) #gives output ['This is line 3']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Once the method .read(4) is called the first 4 characters are called. If we call the method again, the next 4 characters are called. The output for the following cell will demonstrate the process for different inputs to the method read():\n" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "This\n", " is \n", "line 1 \n", "\n", "This is line 2\n" ] } ], "source": [ "# Read certain amount of characters\n", "\n", "with open(example1, \"r\") as file1:\n", " print(file1.read(4))\n", " print(file1.read(4))\n", " print(file1.read(7))\n", " print(file1.read(15))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The process is illustrated in the below figure, and each color represents the part of the file read after the method read() is called:\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " Here is an example using the same file, but instead we read 16, 5, and then 9 characters at a time: \n" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "This is line 1 \n", "\n", "This \n", "is line 2\n" ] } ], "source": [ "# Read certain amount of characters\n", "\n", "with open(example1, \"r\") as file1:\n", " print(file1.read(16))\n", " print(file1.read(5))\n", " print(file1.read(9))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can also read one line of the file at a time using the method readline(): \n" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "first line: This is line 1 \n", "\n" ] } ], "source": [ "# Read one line\n", "\n", "with open(example1, \"r\") as file1:\n", " print(\"first line: \" + file1.readline())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " We can use a loop to iterate through each line: \n" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Iteration 0 : This is line 1 \n", "\n", "Iteration 1 : This is line 2\n", "\n", "Iteration 2 : This is line 3\n" ] } ], "source": [ "# Iterate through the lines\n", "\n", "with open(example1,\"r\") as file1:\n", " i = 0;\n", " for line in file1:\n", " print(\"Iteration\", str(i), \": \", line)\n", " i = i + 1;\n", " \n", " #'lin'e is a word we define and not a reserved word. Code will work if we use the word 'butt'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can use the method readlines() to save the text file to a list: \n" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [], "source": [ "# Read all lines and save as a list\n", "\n", "with open(example1, \"r\") as file1:\n", " FileasList = file1.readlines()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " Each element of the list corresponds to a line of text:\n" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'This is line 1 \\n'" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Print the first line\n", "\n", "FileasList[0]" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'This is line 2\\n'" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Print the second line\n", "\n", "FileasList[1]" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'This is line 3'" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Print the third line\n", "\n", "FileasList[2]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "

The last exercise!

\n", "

Congratulations, you have completed your first lesson and hands-on lab in Python. However, there is one more thing you need to do. The Data Science community encourages sharing work. The best way to share and showcase your work is to share it on GitHub. By sharing your notebook on GitHub you are not only building your reputation with fellow data scientists, but you can also show it off when applying for a job. Even though this was your first piece of work, it is never too early to start building good habits. So, please read and follow this article to learn how to share your work.\n", "

\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Author\n", "\n", "Joseph Santarcangelo\n", "\n", "## Other contributors\n", "\n", "Mavis Zhou\n", "\n", "## Change Log\n", "\n", "| Date (YYYY-MM-DD) | Version | Changed By | Change Description |\n", "| ----------------- | ------- | ---------- | ---------------------------------- |\n", "| 2020-08-26 | 2.0 | Lavanya | Moved lab to course repo in GitLab |\n", "| | | | |\n", "| | | | |\n", "\n", "
\n", "\n", "##

© IBM Corporation 2020. All rights reserved.

\n" ] } ], "metadata": { "kernelspec": { "display_name": "Python", "language": "python", "name": "conda-env-python-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.11" } }, "nbformat": 4, "nbformat_minor": 4 }