[{"data":1,"prerenderedAt":720},["ShallowReactive",2],{"/en-us/blog/learn-python-with-pj-part-4-dictionaries-and-files/":3,"navigation-en-us":35,"banner-en-us":464,"footer-en-us":481,"PJ Metz":691,"next-steps-en-us":705},{"_path":4,"_dir":5,"_draft":6,"_partial":6,"_locale":7,"seo":8,"content":16,"config":25,"_id":28,"_type":29,"title":30,"_source":31,"_file":32,"_stem":33,"_extension":34},"/en-us/blog/learn-python-with-pj-part-4-dictionaries-and-files","blog",false,"",{"title":9,"description":10,"ogTitle":9,"ogDescription":10,"noIndex":6,"ogImage":11,"ogUrl":12,"ogSiteName":13,"ogType":14,"canonicalUrls":12,"schema":15},"Learn Python with Pj! Part 4 - Dictionaries and Files","Our education evangelist Pj Metz continues his journey to learn how to code in Python.","https://res.cloudinary.com/about-gitlab-com/image/upload/v1749664962/Blog/Hero%20Images/python.jpg","https://about.gitlab.com/blog/learn-python-with-pj-part-4-dictionaries-and-files","https://about.gitlab.com","article","\n                        {\n        \"@context\": \"https://schema.org\",\n        \"@type\": \"Article\",\n        \"headline\": \"Learn Python with Pj! Part 4 - Dictionaries and Files\",\n        \"author\": [{\"@type\":\"Person\",\"name\":\"PJ Metz\"}],\n        \"datePublished\": \"2022-05-05\",\n      }",{"title":9,"description":10,"authors":17,"heroImage":11,"date":19,"body":20,"category":21,"tags":22},[18],"PJ Metz","2022-05-05","This is the fourth installment in the Learn Python with Pj! series. Make\nsure to read:\n\n- [Part 1 - Getting started](/blog/learn-python-with-pj-part-1/)\n\n- [Part 2 - Lists and loops](/blog/learn-python-with-pj-part-2/)\n\n- [Part 3 - Functions and\nstrings](/blog/learn-python-with-pj-part-3/)\n\n- [Part 5 - Build a hashtag tracker with the Twitter\nAPI](/blog/learn-python-with-pj-part-5-building-something-with-the-twitter-api/)\n\n\nI’ve learned a lot with Python so far, but when I learned dictionaries\n(sometimes shortened to dicts), I was really excited about what could be\ndone. A dictionary in Python is a series of keys and values stored inside a\nsingle object. This is kind of like a super array; one that allows you to\nconnect keys and values together in a single easily accessible source.\nCreating dictionaries from arrays can actually be very simple, too.\n\n\nIn this blog, I'll dig into how to create dictionaries and how to read and\nwrite files in the code.\n\n\n## Dictionaries\n\n\nDictionaries in Python are indicated by using curly braces, or as I like to\ncall them, mustaches. `{ }` indicates that the list you’re looking at isn’t\na list at all, but a dictionary. \n\n\n```python\n\nshows_and _characters = {\n    \"Bojack Horseman\": \"Todd\",\n    \"My Hero Academia\": \"Midoriya\"\n    \"Ozark\": \"Ruth\"\n    \"Arrested Development\": \"Tobias\",\n    \"Derry Girls\": \"Sister Michael\",\n    \"Tuca & Bertie\": \"Bertie\"\n    }\n```\n\n\nThis is a dictionary of my favorite TV shows and my favorite characters in\nthat show. In this example, the key is on the left and the value is on the\nright. To access dictionaries, you use a similar call like you would for a\nlist, except instead of an element number, you would put the key.\n`print(shows_and_characters[“Ozark”])` would print `Ruth` to the console.\nAdditionally, both the key and value in this example are strings, but that’s\nnot a requirement. Keys can be any immutable type, like strings, ints,\nfloats, and tuples. Values don’t have this same restriction, therefore\nvalues can be a nested dictionary or a list, in addition to the types\nmentioned for keys. For instance, the following dictionary is a valid\ndictionary.\n\n\n```python\n\nshows_with_lists = {\n    \"Bojack Horseman\": [\"Todd\", \"Princess Carolyn\", \"Judah\", \"Diane\"],\n    \"My Hero Academia\": [\"Midoriya\", \"Shoto\", \"All Might\", \"Bakugo\", \"Kirishima\"],\n    \"Ozark\": [\"Ruth\", \"Jonah\", \"Wyatt\"],\n    \"Arrested Development\": [\"Tobias\", \"Gob\", \"Anne\", \"Maeby\"],\n    \"Derry Girls\": [\"Sister Michael\", \"Orla\", \"Erin\", \"Claire\", \"James\"],\n    \"Tuca & Bertie\": [\"Bertie\", \"Speckle\", \"Tuca\", \"Dakota\"]\n    }\n```\n\nIn this example, each value is a list. So if we tried to print the value for\nthe key `”Derry Girls”`, we would see `[“Sister Michael”, “Orla”, “Erin”,\n“Claire”, “James”]` printed to the console. However, if we wanted the last\nelement in the value list, we’d write `shows_with_lists[“Derry Girls”]\n[-1]`. This would print the last element in the list, which in this case is\n`James`. \n\n\nDictionaries can be written manually, or, if you have two lists, you can\ncombine the `dict()` and `zip()` methods to make the lists into a\ndictionary. \n\n\n```python\n\nlist_of_shows = [\"Bojack Horseman\",\n                 \"My Hero Academia\",\n                 \"Ozark\",\n                 \"Arrested Development\",\n                 \"Derry Girls\",\n                 \"Tuca & Bertie\"]\nlist_of_characters = [[\"Todd\", \"Princess Carolyn\", \"Judah\", \"Diane\"],\n                      [\"Midoriya\", \"Shoto\", \"All Might\", \"Bakugo\", \"Kirishima\"],\n                      [\"Ruth\", \"Jonah\", \"Wyatt\"],\n                      [\"Tobias\", \"Gob\", \"Anne\", \"Maeby\"],\n                      [\"Sister Michael\", \"Orla\", \"Erin\", \"Claire\", \"James\"],\n                      [\"Bertie\", \"Speckle\", \"Tuca\", \"Dakota\"]]\n\ncombined_shows_characters = dict(zip(list_of_shows, list_of_characters))\n\n\nprint(combined_shows_characters)\n\n```\n\n\nThis is one way to create a dictionary. Another is called Dictionary\nComprehension. This one is a little more work, but can be used in a variety\nof different ways, including using a bit of logic on a single list to\ngenerate a dictionary using that original list. Here’s how with two\nexamples: one based on the above lists, and one with a single list and some\nlogic. \n\n\n```python\n\nimport math\n\n\n#This is doing the same work as the above example, but using Dict\nComprehension instead. \n\ncomprehension_shows_characters = { shows:characters for shows, characters in\nzip(list_of_shows, list_of_characters)  }\n\n\nhip_to_be_square = [4, 9, 16, 25, 36, 49]\n\n\nno_longer_hip_to_be_square = { key:math.sqrt(key) for key in\nhip_to_be_square }\n\n\nprint(no_longer_hip_to_be_square)\n\n```\n\n\nIn the `no_longer_hip_to_be_square` dictionary, the key is found in the\n`hip_to_be_square` list. The value for each key is its own square root,\nbrought in with the import math function. There are plenty more useful\nmethods for dealing with dictionaries\n[here](https://realpython.com/python-dicts/). \n\n\n## Reading and writing files\n\n\nThis one is a pretty cool part of Python: reading and writing other files\nright in the code. With Python, you’re able to take the contents of certain\ntypes of files and use it in your code, or even create a new file based on\nsome input. This is useful for data handling and can be used with a  variety\nof file types. The two I’ll be covering here are .csv and .txt.\n\n\n### Reading from a file\n\n\nImagine a .txt file named `best-ever.txt` containing the line `My favorite\ntv show is Derry Girls`. We can use Python to take that line and turn it\ninto a variable. Running the following code would print the contents of the\n.txt file to the terminal. \n\n\n```python\n\nwith open(\"best-ever.txt\") as text_file:\n  text_data = text_file.read()\n\n#This will print the contents of the .txt file. \n\nprint(text_data)\n\n```\n\n\nBy using `with open(NAME OF FILE) as VARIABLE_NAME:`, we can examine the\ncontents of files as a single string. If the document has multiple lines,\nyou can even separate those by iterating over them by using a for loop and\nthe `.readlines()` method. Using an imaginary .txt document called\n`buncha-lines` we could use the following to print out each line\nindividually.\n\n\n```python\n\nwith open(\"buncha-lines.txt\") as lines_doc:\n  for line in lines_doc.readlines():\n    print(line)\n``` \n\n### Writing a new file\n\n\nCreating a new file is also easy with Python. The `open()` function can take\nan additional argument in order to create a new file. In fact, there’s a\ndefault argument that’s been being passed each time without us knowing! `r`\nis the default argument for `open()` and puts it in read mode. To turn on\nwrite mode, pass in a `w` as the second argument. The following code will\nwrite a brand-new file called `best_tv_character.txt` with the contents\n`Peggy Olson from Mad Men`. \n\n\n```python\n\nwith open(\"best_tv_character.txt\", \"w\") as best_character:\n  best_character.write(\"Peggy Olson from Mad Men\")\n```\n\n### Working with .csv files\n\n\nYou can read a .csv file with Python by using `import csv` at the beginning\nof the file, and then using some of its built-in methods in the code.\nHowever, even though .csv files are plain text, treating a .csv file the\nsame as you treat .txt files can lead to difficult to read outputs; after\nall, the point of a spreadsheet is to table information. Without that table,\nthe output can be chaotic. A way around this is to use the `dictreader()`\nmethod. This method allows you to map the information in each row to a\ndictionary with field names you can create. The default field names are\ncollected from the first row of the .csv if no field names are given.\nImagine a .csv file with columns labeled, “Network”, “Show name”, “Seasons”.\nMaybe we just want to print the number of seasons from this .csv. \n\n\n```python\n\nimport csv \n\n\nwith open(\"shows.csv\") as shows_csv:\n  shows_dict = csv.DictReader(shows_csv)\n  for row in shows_dict:\n    print(row[\"Seasons\"])\n```\n\n\nThis would print to the console, on a new line, the number of seasons for\neach row that exists in the .csv. \n\n\nJust like with .txt files, you can also create .csv files with Python. It’s\na bit more complicated since you need to define the headers, or column\nnames, but it is still a quick process. This can be used to take lists and\nturn them into .csv files. Let’s check out the following example:\n\n\n```python\n\nimport csv\n\n\nworking_list = [{\"Network\": \"Netflix\", \"Show Name\":\"Bojack Horseman\",\n\"Seasons\":6}, {\"Network\":\"Channel 4\",\"Show Name\":\"Derry Girls\", \"Seasons\":\n3}, {\"Network\":\"HBO Max\", \"Show Name\":\"Our Flag Means Death\", \"Seasons\": 1}]\n\n\n\nwith open(\"shows.csv\", \"w\") as shows_csv:\n    fields = [\"Network\", \"Show Name\", \"Seasons\"]\n    shows_w = csv.DictWriter(shows_csv, fieldnames = fields)\n\n    shows_w.writeheader()\n    for item in working_list:\n        shows_w.writerow(item)\n```\n\n\nThis previous code block creates a brand-new csv file by using the `”w”`\nparameter in `open()`. We manually name the fields in the order they appear\nin a separate list, then pass that list into the `DictWriter` parameter\n`fieldnames`. Finally, we use the `writeheader()` and a for loop with the\n`writerow()` methods to create a header row and to iterate over the\n`working_list` and turn each entry into a row in the .csv. \n\n\nThese are only a few ways to work with .csv and .txt files; Python is very\nversatile and more information [can be found\nhere](https://realpython.com/working-with-files-in-python/).\n","careers",[21,23,24],"tutorial","growth",{"slug":26,"featured":6,"template":27},"learn-python-with-pj-part-4-dictionaries-and-files","BlogPost","content:en-us:blog:learn-python-with-pj-part-4-dictionaries-and-files.yml","yaml","Learn Python With Pj Part 4 Dictionaries And 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