diff --git a/.ipynb_checkpoints/README-checkpoint.md b/.ipynb_checkpoints/README-checkpoint.md new file mode 100644 index 0000000..fd0bbb5 --- /dev/null +++ b/.ipynb_checkpoints/README-checkpoint.md @@ -0,0 +1,85 @@ +![Ironhack logo](https://i.imgur.com/1QgrNNw.png) + +# Lab | Data Cleaning + +## Introduction + +We keep seeing a common phrase that 80% of the work of a data scientist is data cleaning. We have no idea whether this number is accurate but a data scientist indeed spends lots of time and effort in collecting, cleaning and preparing the data for analysis. This is because datasets are usually messy and complex in nature. It is a very important ability for a data scientist to refine and restructure datasets into a usable state in order to proceed to the data analysis stage. + +In this exercise, you will both practice the data cleaning techniques we discussed in the lesson and learn new techniques by looking up documentations and references. You will work on your own but remember the teaching staff is at your service whenever you encounter problems. + +## Getting Started + +Now you should already be familar with the workflow of solving and submitting the labs. But in case not, review the guidelines in the `README.md` in the [repo root](../..) and [previous lab](../lab-pandas). + +In this lab you will be working on [main.ipynb](your-code/main.ipynb). To launch it, first navigate to the directory that contains `main.ipynb` in Terminal, then execute `jupyter notebook`. In the webpage that is automatically opened, click the `main.ipynb` link to launch it. + +When you are on `main.ipynb`, read the instructions for each cell and provide your answers. Make sure to test your answers in each cell and save. Jupyter Notebook should automatically save your work progress. But it's a good idea to periodically save your work manually just in case. + +## Goals + +Do you remember your MySQL project? In this lab, you will examine some MySQL tables from [here](https://relational.fit.cvut.cz/dataset/Stats). This database contains an anonymized dump of all user-contributed content on the Stats Stack Exchange network. + +You will need to import the `pymysql` library and the `create_engine` function from the `sqlalchemy` library. + +```python +import pymysql +from sqlalchemy import create_engine +``` + +Once your connection is established with the database yoo will use some basic SELECT queries to retrieve the data in order to answer the questions described next. + +:bulb: If you receive import errors for `pymysql` or `sqlalchemy`, it means you need to install them with `pip`. + +### Challenge Questions + +1. Connect to the server and collect all the data from users and posts tables. + +1. Create a merged dataframe with users and post tables. **Take into account that you will need to do some stuff before merging.** + +1. Identify missing values in the merged dataframe and apply some of the methods. + +1. Change the data types of your merged dataset accordingly. + +1. Bonus Question: Create a dataframe with the outliers you have identified in the dataframe and export it to a csv file in your-code folder. + +**:exclamation: If you feel you are already good at Python/Pandas and don't need the instructions in `main.ipynb` to walk you through, please feel free to skip `main.ipynb` and create your own solution file.** + +## Deliverables + +- `main.ipynb` with your responses to each of the questions above. +- `outliers.csv` containing the outliers of the dataset. +- `weather.ipynb` containing the additional challenge code and results. + +## Submission + +Upon completion, add your deliverables to git. Then commit git, push to your forked repo, and create the pull request as in the previous labs. **REMEMBER + +- Upon completion, commit your code and submit to github. **REMEMBER YOU HAVE ALREADY FORKED THE REPO BEFORE**!! + + ``` + git add . + git commit -m "" + git push origin master + ``` + +- Navigate to your repo and [create a Pull Request](https://help.github.com/articles/creating-a-pull-request/). +- Create a pull request with title following this format: **"[][] []"** + - For instance, if you are doing data bootcamp in Madrid, your name is Marc Pomar and the lab you are working on is `lab-numpy`, your pull request should be named like this: "[MAD][datamad10108] [lab-numpy] Marc Pomar" +- If you have successfully created the pull request you are done! CONGRATS :) + +## Resources + +[Data Cleaning with Numpy and Pandas](https://realpython.com/python-data-cleaning-numpy-pandas/#python-data-cleaning-recap-and-resources) + +[Data Cleaning Video](https://www.youtube.com/watch?v=ZOX18HfLHGQ) + +[Data Preparation](https://www.kdnuggets.com/2017/06/7-steps-mastering-data-preparation-python.html) + +[Google Search](https://www.google.es/search?q=how+to+clean+data+with+python) + +## Additional Challenges for the Nerds + +If you have completed the `Stats` challenge without much difficulty, you can try to tidy the data you will find in thie lab folder [weather](../weather-raw.csv). This dataset is a subset of a global historical climatology network dataset. The data represents the daily weather records for a weather station (MX17004) in Mexico for five months in 2010. The goal of this additional challenge is to get the most tidy dataset you are able to produce. **Hint:Variables are stored in both rows and columns.** + +To accomplish this challenge, you will need to do some research on tidying and melt&pivot. Feel free to reference any resources you consider appropiate. diff --git a/Outliers.csv b/Outliers.csv new file mode 100644 index 0000000..c60c91f --- /dev/null +++ b/Outliers.csv @@ -0,0 +1,4397 @@ +,userId,Reputation,Views,UpVotes,DownVotes,postId,Score,ViewCount,CommentCount,Votes +8724,805,65272,5680,7035,143,1425,11,0.0,3,6892 +8725,805,65272,5680,7035,143,1723,8,0.0,4,6892 +8726,805,65272,5680,7035,143,1724,5,0.0,0,6892 +8727,805,65272,5680,7035,143,2434,2,0.0,4,6892 +8728,805,65272,5680,7035,143,2589,2,0.0,0,6892 +8729,805,65272,5680,7035,143,2604,8,0.0,5,6892 +8730,805,65272,5680,7035,143,3154,5,0.0,0,6892 +8731,805,65272,5680,7035,143,3579,2,0.0,0,6892 +8732,805,65272,5680,7035,143,3625,3,0.0,0,6892 +8733,805,65272,5680,7035,143,3735,6,0.0,0,6892 +8734,805,65272,5680,7035,143,9603,0,0.0,2,6892 +8735,805,65272,5680,7035,143,9855,0,0.0,0,6892 +8736,805,65272,5680,7035,143,14900,2,0.0,1,6892 +8737,805,65272,5680,7035,143,15825,2,0.0,0,6892 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a/your-code/main.ipynb +++ b/your-code/main.ipynb @@ -9,10 +9,12 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "import pandas as pd" + ] }, { "cell_type": "markdown", @@ -23,10 +25,13 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "import pymysql\n", + "from sqlalchemy import create_engine" + ] }, { "cell_type": "markdown", @@ -37,10 +42,12 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "engine= create_engine ('mysql+pymysql://guest:relational@relational.fit.cvut.cz:3306/stats')" + ] }, { "cell_type": "markdown", @@ -51,10 +58,12 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "datasql = pd.read_sql_query('SELECT * FROM stats.users', engine)" + ] }, { "cell_type": "markdown", @@ -65,10 +74,280 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/html": [ + "
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674292010-07-19 19:04:37Vince2014-09-10 21:14:12http://bioinformatics.ucdavis.eduDavis, CAI'm a recent graduate of UC Davis in Economics...562004951428.0None
7867642010-07-19 19:04:52csgillespie2014-09-09 21:15:08http://www.mas.ncl.ac.uk/~ncsg3/Newcastle, United Kingdom<p>I'm a statistics lecturer at Newcastle Univ...1089604257000236.0None
8101212010-07-19 19:05:40Pierre2014-07-28 18:15:02http://plindenbaum.blogspot.comFranceBioinformatician\\\\nVirology\\\\nGenetics\\\\nBiolo...20202323444.0None
9111362010-07-19 19:06:02wahalulu2014-07-23 21:28:18http://www.linkedin.com/in/marckvaismanWashington, DC<p>data_stuff &lt;- paste('data', c('scientist...101004789340.0None
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    data_stuff <- paste('data', c('scientist... 10 10 \n", + "\n", + " DownVotes AccountId Age ProfileImageUrl \n", + "0 1920 -1 NaN None \n", + "1 0 2 37.0 None \n", + "2 0 3 35.0 None \n", + "3 0 1998 28.0 http://i.stack.imgur.com/d1oHX.jpg \n", + "4 5 54503 35.0 None \n", + "5 0 46050 41.0 None \n", + "6 0 49514 28.0 None \n", + "7 25 70002 36.0 None \n", + "8 0 23234 44.0 None \n", + "9 0 47893 40.0 None " + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "datasql.rename(columns={'Id':'userId'}, inplace=True)\n", + "datasql.head(10)" + ] }, { "cell_type": "markdown", @@ -79,10 +358,12 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 11, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "postsql = pd.read_sql_query('SELECT * FROM stats.posts', engine)" + ] }, { "cell_type": "markdown", @@ -93,10 +374,384 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 14, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/html": [ + "

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    67118.02010-07-19 19:15:59765808.0<p>I've been working on a new method for analy...38.02013-12-28 06:53:10Locating freely available data samples...24.0379.0253.02013-09-26 21:50:362010-07-20 20:50:48NaNNaTNoneNone
    781NaN2010-07-19 19:16:210288.0<p>Sorry, but the emptyness was a bit overwhel...37.02010-10-18 07:57:31So how many staticians *does* it take to screw......1.02NaN449.02010-10-18 07:57:31NaTNaN2010-07-19 20:19:46NoneNone
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    91011887.02010-07-19 19:17:472321925.0<p>Many studies in the social sciences use Lik...24.02012-10-23 17:33:41Under what conditions should Likert scales be ......4.0412.0919.02011-03-30 15:31:46NaTNaNNaTNoneNone
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    userIdReputationViewsUpVotesDownVotespostIdScoreViewCountCommentCountVotes
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    " + ], + "text/plain": [ + " userId Reputation Views UpVotes DownVotes postId Score ViewCount \\\n", + "0 -1 1 0 5007 1920 2175 0 NaN \n", + "1 -1 1 0 5007 1920 8576 0 NaN \n", + "2 -1 1 0 5007 1920 8578 0 NaN \n", + "3 -1 1 0 5007 1920 8981 0 NaN \n", + "4 -1 1 0 5007 1920 8982 0 NaN \n", + ".. ... ... ... ... ... ... ... ... \n", + "995 71 1936 328 2317 1 11240 2 NaN \n", + "996 71 1936 328 2317 1 15547 1 NaN \n", + "997 71 1936 328 2317 1 15679 7 NaN \n", + "998 71 1936 328 2317 1 15763 3 NaN \n", + "999 71 1936 328 2317 1 17849 4 NaN \n", + "\n", + " CommentCount Votes \n", + "0 0 3087 \n", + "1 0 3087 \n", + "2 0 3087 \n", + "3 0 3087 \n", + "4 0 3087 \n", + ".. ... ... \n", + "995 6 2316 \n", + "996 2 2316 \n", + "997 1 2316 \n", + "998 0 2316 \n", + "999 0 2316 \n", + "\n", + "[1000 rows x 10 columns]" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "userpost = pd.merge(userinfo, postinfo, on='userId')\n", + "userpost['Votes'] = userpost['UpVotes'] - userpost['DownVotes']\n", + "userpost.head(1000)" + ] }, { "cell_type": "markdown", @@ -138,10 +1017,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 30, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/plain": [ + "ViewCount 48396\n", + "dtype: int64" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#userpostsort = userpost[(userpost['ViewCount']>1)]\n", + "null_cols = userpost.isnull().sum()\n", + "null_cols[null_cols > 0]" + ] }, { "cell_type": "markdown", @@ -153,10 +1048,12 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 72, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "userpost[['ViewCount']] = userpost[['ViewCount']].fillna(0)" + ] }, { "cell_type": "markdown", @@ -167,10 +1064,33 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 73, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/plain": [ + "userId int64\n", + "Reputation int64\n", + "Views int64\n", + "UpVotes int64\n", + "DownVotes int64\n", + "postId int64\n", + "Score int64\n", + "ViewCount float64\n", + "CommentCount int64\n", + "Votes int64\n", + "dtype: object" + ] + }, + "execution_count": 73, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "userpost.dtypes" + ] }, { "cell_type": "markdown", @@ -178,6 +1098,87 @@ "source": [ "#### Bonus: Identify extreme values in your merged dataframe as you have learned in class, create a dataframe called outliers with the same columns as our data set and calculate the bounds. The values of the outliers dataframe will be the values of the merged_df that fall outside that bounds. You will need to save your outliers dataframe to a csv file on your-code folder." ] + }, + { + "cell_type": "code", + "execution_count": 80, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-61 701.0424688686744 11399\n" + ] + }, + { + "data": { + "text/plain": [ + "userId 39249\n", + "Reputation 39249\n", + "Views 39249\n", + "UpVotes 39249\n", + "DownVotes 39249\n", + "postId 39249\n", + "Score 39249\n", + "ViewCount 39249\n", + "CommentCount 39249\n", + "Votes 39249\n", + "dtype: int64" + ] + }, + "execution_count": 80, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "print(userpost['Votes'].min(), userpost['Votes'].mean(), userpost['Votes'].max())\n", + "#filtered = data[(data['Make']=='Ford')\n", + "userpost[(userpost['Votes']<10)].count()" + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 a 40 28523\n", + "-40 a 0 22363\n", + "800 a 6000 9114\n", + "40 a 100 8284\n", + "100 a 200 6131\n", + "200 a 400 5914\n", + "400 a 800 5471\n", + "6000 a 12000 4396\n", + "-70 a -40 388\n", + "Name: Votes, dtype: int64\n" + ] + } + ], + "source": [ + "#mpg_labels = ['Min','Mean','Max']\n", + "mpg_labels = ['-70 a -40','-40 a 0','0 a 40', '40 a 100', '100 a 200','200 a 400','400 a 800','800 a 6000','6000 a 12000']\n", + "#cutoffs = [userpost['Votes'].min(), userpost['Votes'].mean(),5000, userpost['Votes'].max()]\n", + "cutoffs = [-70,-40,0,40,100,200,400,800,6000,12000]\n", + "bins = pd.cut(userpost['Votes'],cutoffs, labels=mpg_labels)\n", + "print(bins.value_counts())" + ] + }, + { + "cell_type": "code", + "execution_count": 99, + "metadata": {}, + "outputs": [], + "source": [ + "outliers = userpost[(userpost['Votes']>6000)]\n", + "outliers.head(100000)\n", + "outliers.to_csv(r'C:\\users\\joavi\\Documents\\lab-data-cleaning\\Outliers.csv')" + ] } ], "metadata": { @@ -196,9 +1197,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.5" + "version": "3.7.6" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/your-code/outliers.csv b/your-code/outliers.csv index e69de29..c60c91f 100644 --- a/your-code/outliers.csv +++ b/your-code/outliers.csv @@ -0,0 +1,4397 @@ +,userId,Reputation,Views,UpVotes,DownVotes,postId,Score,ViewCount,CommentCount,Votes +8724,805,65272,5680,7035,143,1425,11,0.0,3,6892 +8725,805,65272,5680,7035,143,1723,8,0.0,4,6892 +8726,805,65272,5680,7035,143,1724,5,0.0,0,6892 +8727,805,65272,5680,7035,143,2434,2,0.0,4,6892 +8728,805,65272,5680,7035,143,2589,2,0.0,0,6892 +8729,805,65272,5680,7035,143,2604,8,0.0,5,6892 +8730,805,65272,5680,7035,143,3154,5,0.0,0,6892 +8731,805,65272,5680,7035,143,3579,2,0.0,0,6892 +8732,805,65272,5680,7035,143,3625,3,0.0,0,6892 +8733,805,65272,5680,7035,143,3735,6,0.0,0,6892 +8734,805,65272,5680,7035,143,9603,0,0.0,2,6892 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index 4fc40ab..cd610dd 100644 --- a/your-code/weather.ipynb +++ b/your-code/weather.ipynb @@ -61,9 +61,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.5" + "version": "3.7.6" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 }