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analyzing lesson

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Jasmine 2021-09-24 16:41:00 -04:00
parent e90da3ad33
commit d751a63626
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33
4-Data-Science-Lifecycle/14-Introduction/notebook.ipynb
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@ -31,7 +31,7 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 2,
"source": [
"import pandas as pd\r\n",
"import glob\r\n",
@ -42,9 +42,36 @@
"\r\n",
"df = pd.concat([january_taxi, july_taxi])\r\n",
"\r\n",
"print(df)"
"# df.describe()\r\n",
"print(df.describe())"
],
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"c:\\Users\\jasmineg\\projects\\Data-Science-For-Beginners\\venv\\lib\\site-packages\\IPython\\core\\interactiveshell.py:3441: DtypeWarning: Columns (6) have mixed types.Specify dtype option on import or set low_memory=False.\n",
" exec(code_obj, self.user_global_ns, self.user_ns)\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
" VendorID passenger_count ... total_amount congestion_surcharge\n",
"count 1.394425e+07 1.394425e+07 ... 1.397821e+07 9.122233e+06\n",
"mean 1.641553e+00 1.569314e+00 ... 1.745644e+01 1.563450e+00\n",
"std 5.172343e-01 1.219889e+00 ... 1.945658e+02 1.215658e+00\n",
"min 1.000000e+00 0.000000e+00 ... -4.508000e+02 -2.500000e+00\n",
"25% 1.000000e+00 1.000000e+00 ... 9.360000e+00 0.000000e+00\n",
"50% 2.000000e+00 1.000000e+00 ... 1.295000e+01 2.500000e+00\n",
"75% 2.000000e+00 2.000000e+00 ... 1.880000e+01 2.500000e+00\n",
"max 4.000000e+00 9.000000e+00 ... 6.232617e+05 2.750000e+00\n",
"\n",
"[8 rows x 15 columns]\n"
]
}
],
"outputs": [],
"metadata": {}
}
],

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4-Data-Science-Lifecycle/15-analyzing/README.md
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[Pre-lecture quiz]()
Analyzing in the data lifecycle confirms that the data can answer the questions that are proposed or solving a particular problem. This step can also focus on confirming a model is correctly addressing these questions and problems. This lesson is focused on Exploratory Data Analysis or EDA, which are techniques for defining features and relationships within the data and can be used to prepare the data for modeling.
We'll be using an example dataset from [Kaggle](https://www.kaggle.com/balaka18/email-spam-classification-dataset-csv/version/1) to show how this can be applied with Python's Pandas library. This dataset contains a count of some common words found in emails, the sources of these emails are anonymous. Use the notebook in this directory to follow along.
## Exploratory Data Analysis
The capture phase of the lifecycle is where the data is acquired as well as the problems and questions at hand, but how do we know the data can help support the end result?
Recall that a data scientist may ask the following questions when they acquire the data:
- Do I have enough data to solve this problem?
- Is the data of acceptable quality for this problem?
- If I discover additional information through this data, should we consider changing or redefining the goals?
Exploratory Data Analysis is the process of getting to know that data and can be used to answer these questions, as well identify the challenges of working with the dataset. Let’s focus on some of the techniques used to achieve this.
## Data Profiling, Descriptive Statistics, and Pandas
How do we evaluate if we have enough data to solve this problem? Data profiling can summarize and gather some general overall information about our dataset through techniques of descriptive statistics. Data profiling helps us understand what is available to us, and descriptive statistics helps us understand how many things are available to us.
In the few of the lessons, we have used Pandas to provide some descriptive statistics with the [`describe()` function]( https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.describe.html). It provides the count, max and min values, mean, standard deviation and quantiles on the numerical data. Using descriptive statistics like the `describe()` function can help you assess how much you have and if you need more.
## Sampling and Querying
Exploring everything in a large dataset can be very time consuming and a task that’s usually left up to a computer to do. However, sampling is a helpful tool in understanding of the data and allows us to have a better understanding of what’s in the dataset and what it represents. With a sample, you can apply probability and statistics to come to some general conclusions about your data. While there’s no defined rule on how much data you should sample it’s important to note that the more data you sample, the more precise of a generalization you can make of about data.
Pandas has the [`sample()` function in its library]( https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.sample.html) where you can pass an argument of how many random samples you’d like to receive and use.
[example]
General querying of the data can help you answer some general questions and theories you may have. In contrast to sampling, queries allow you to have control and focus on specific parts of the data you have questions about.
The [`query() `function]( https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.query.html) in the Pandas library allows you to select the columns and receive simple answers about the data in the form of `True` or `False`.
[example]
## Exploring with Visualizations
You don’t have to wait until the data is thoroughly cleaned and analyzed to start creating visualizations. In fact, having a visual representation while exploring can help identify patterns, relationships, and problems in the data. Furthermore, visualizations provide a means of communication with those who are not involved with managing the data and can be an opportunity to share and clarify additional questions that were not addressed in the capture stage. Refer to the section on Visualization to learn more about some popular ways to explore visually.
[example]
## Exploring to identify inconsistencies
All the topics in this lesson can help identify missing or inconsistent values, but Pandas provides functions to check for some of these. [isna() or isnull()](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.isna.html) can check for missing values.
One important piece of exploring for these values within your data is to explore why they ended up that way in the first place. This can help you decide on what [actions to take to resolve them]().
## 🚀 Challenge

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4-Data-Science-Lifecycle/15-analyzing/notebook.ipynb
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@ -1,23 +1,60 @@
{
"cells": [
{
"cell_type": "markdown",
"source": [],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": null,
"source": [
"# print(pd.read_csv('../../data/Taxi/yellow_tripdata_2019-01.csv'))\r\n",
"# all_files = glob.glob('../../data/Taxi/*.csv')\r\n",
"import pandas as pd\r\n",
"import glob\r\n",
"\r\n",
"# df = pd.concat((pd.read_csv(f) for f in all_files))\r\n",
"# print(df)"
"#Loading the dataset\r\n",
"path = '../../data/emails.csv'\r\n",
"email_df = pd.read_csv(path)"
],
"outputs": [],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": null,
"source": [
"# Using Describe on the email dataset\r\n",
"print(email_df.describe())"
],
"outputs": [],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [],
"metadata": {}
}
],
"metadata": {
"orig_nbformat": 4,
"language_info": {
"name": "python"
"name": "python",
"version": "3.9.7",
"mimetype": "text/x-python",
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"pygments_lexer": "ipython3",
"nbconvert_exporter": "python",
"file_extension": ".py"
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3.9.7 64-bit ('venv': venv)"
},
"interpreter": {
"hash": "6b9b57232c4b57163d057191678da2030059e733b8becc68f245de5a75abe84e"
}
},
"nbformat": 4,

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data/emails.csv 100644
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Email No.,the,to,ect,and,for,of,a,you,in,on,is,this,i,be,that,will
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Email 26,12,53,2,14,18,14,287,0,86,50,47,6,300,7,3,0
Email 27,5,4,1,1,4,4,51,0,8,6,6,2,44,6,0,0
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Email 36,3,2,1,0,1,1,25,1,4,5,2,1,15,1,1,0
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Email 38,5,1,2,1,1,0,19,1,0,7,1,0,16,2,1,0
Email 39,7,2,1,3,1,3,27,1,3,4,3,1,18,1,0,1
Email 40,8,4,1,2,4,1,38,6,11,1,4,0,65,2,1,13
Email 41,2,3,1,5,1,0,45,0,8,6,10,2,37,2,0,0
Email 42,5,5,8,2,2,0,41,1,2,6,4,1,21,3,2,1
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Email 70,23,12,11,3,15,2,128,1,28,28,11,4,79,6,2,0
Email 71,2,1,1,0,0,0,8,1,3,6,3,1,18,0,1,1
Email 72,0,6,1,0,0,0,19,0,5,7,4,0,27,0,0,0
Email 73,0,2,1,4,1,0,37,4,6,14,4,0,43,2,0,0
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Email 79,3,1,1,0,1,1,19,0,2,6,1,1,12,2,0,1
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Email 84,12,15,1,12,7,1,231,10,51,33,4,1,290,3,1,1
Email 85,7,1,4,2,0,2,28,1,6,6,6,1,21,2,2,0
Email 86,8,3,13,4,2,4,45,1,3,5,5,2,22,2,0,1
Email 87,0,0,1,0,1,0,3,0,0,2,0,0,0,1,0,0
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Email 94,19,23,4,20,12,11,190,12,28,28,14,0,125,8,2,2
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Email 100,3,3,1,1,0,3,35,2,4,6,4,0,28,0,0,1
Email 101,3,2,1,2,2,1,24,0,4,1,3,2,15,0,1,0
Email 102,2,2,2,0,2,0,19,1,3,4,3,0,16,2,1,0
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Email 115,1,2,1,2,2,0,14,1,1,2,1,0,7,4,0,0
Email 116,33,16,8,7,16,21,387,11,45,88,40,7,294,5,2,1
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Email 379,3,2,7,2,6,0,45,1,7,6,2,1,17,1,0,1
Email 5146,2,0,1,2,7,1,68,1,10,11,2,0,44,0,0,0
Email 5147,0,3,2,0,0,0,7,0,1,1,0,0,3,0,0,0
Email 5148,21,20,2,0,4,4,84,3,15,14,7,2,63,9,2,0
Email 5149,12,6,1,2,5,1,61,1,9,9,2,0,40,1,4,2
Email 5150,10,9,4,7,2,0,107,2,20,15,1,0,63,3,2,2
Email 5151,16,10,3,14,13,7,161,5,26,31,15,2,119,3,0,0
Email 5152,4,4,1,3,2,0,49,1,9,6,0,0,25,1,0,0
Email 5153,2,7,3,0,2,3,37,0,13,18,0,0,43,2,1,1
Email 5154,1,2,4,3,1,0,25,1,2,5,8,0,25,1,0,0
Email 5155,3,5,1,2,1,1,28,2,4,6,4,1,19,1,0,0
Email 5156,7,5,1,0,4,1,36,4,12,2,3,2,32,4,6,3
Email 5157,4,13,1,0,3,1,48,2,8,26,9,1,45,1,0,0
Email 5158,14,10,4,3,6,2,107,1,13,15,4,0,57,3,4,2
Email 5159,2,1,1,0,1,1,16,0,2,4,3,0,13,1,0,0
Email 5160,2,13,1,0,2,1,38,2,7,24,6,1,34,1,0,0
Email 5161,10,5,2,5,2,2,61,6,14,13,15,2,67,3,1,3
Email 5162,59,30,4,16,9,31,195,0,40,56,17,2,167,13,3,2
Email 5163,2,3,1,2,1,2,32,0,7,3,2,0,26,3,0,0
Email 5164,0,0,1,0,0,0,1,0,0,0,0,0,1,0,0,0
Email 5165,21,18,3,1,6,4,106,1,18,11,4,3,70,6,3,1
Email 5166,1,0,1,0,3,1,12,1,2,2,1,0,7,0,0,0
Email 5167,1,0,1,1,0,0,4,0,0,0,1,0,1,0,0,0
Email 5168,2,2,2,3,0,0,32,0,5,6,1,0,20,1,0,0
Email 5169,35,27,11,2,6,5,151,4,23,18,6,3,98,10,6,2
Email 5170,0,0,1,1,0,0,11,0,1,1,3,1,10,0,0,0
Email 5171,2,7,1,0,2,1,28,2,8,11,7,1,39,1,0,0
Email 5172,22,24,5,1,6,5,148,8,23,13,5,4,99,6,4,1
1 Email No. the to ect and for of a you in on is this i be that will
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