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[#]: collector: (lujun9972)
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[#]: translator: (GraveAccent)
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[#]: reviewer: ( )
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[#]: publisher: ( )
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[#]: url: ( )
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[#]: subject: (Getting started with data science using Python)
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[#]: via: (https://opensource.com/article/19/9/get-started-data-science-python)
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[#]: author: (Seth Kenlon https://opensource.com/users/sethhttps://opensource.com/users/jroakeshttps://opensource.com/users/tiwarinitish86)
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Getting started with data science using Python
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======
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Doing data science with Python offers limitless potential for you to
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parse, interpret, and structure data in meaningful and enlightening
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ways.
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![Metrics and a graph illustration][1]
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Data science is an exciting new field in computing that's built around analyzing, visualizing, correlating, and interpreting the boundless amounts of information our computers are collecting about the world. Of course, calling it a "new" field is a little disingenuous because the discipline is a derivative of statistics, data analysis, and plain old obsessive scientific observation.
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But data science is a formalized branch of these disciplines, with processes and tools all its own, and it can be broadly applied across disciplines (such as visual effects) that had never produced big dumps of unmanageable data before. Data science is a new opportunity to take a fresh look at data from oceanography, meteorology, geography, cartography, biology, medicine and health, and entertainment industries and gain a better understanding of patterns, influences, and causality.
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Like other big and seemingly all-inclusive fields, it can be intimidating to know where to start exploring data science. There are a lot of resources out there to help data scientists use their favorite programming languages to accomplish their goals, and that includes one of the most popular programming languages out there: Python. Using the [Pandas][2], [Matplotlib][3], and [Seaborn][4] libraries, you can learn the basic toolset of data science.
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If you're not familiar with the basics of Python yet, read my [introduction to Python][5] before continuing.
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### Creating a Python virtual environment
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Programmers sometimes forget which libraries they have installed on their development machine, and this can lead them to ship code that worked on their computer but fails on all others for lack of a library. Python has a system designed to avoid this manner of unpleasant surprise: the virtual environment. A virtual environment intentionally ignores all the Python libraries you have installed, effectively forcing you to begin development with nothing more than stock Python.
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To activate a virtual environment with **venv**, invent a name for your environment (I'll use **example**) and create it with:
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```
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`$ python3 -m venv example`
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```
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Source the **activate** file in the environment's **bin** directory to activate it:
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```
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$ source ./example/bin/activate
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(example) $
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```
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You are now "in" your virtual environment, a clean slate where you can build custom solutions to problems—with the added burden of consciously needing to install required libraries.
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### Installing Pandas and NumPy
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The first libraries you must install in your new environment are Pandas and NumPy. These libraries are common in data science, so this won't be the last time you'll install them. They're also not the only libraries you'll ever need in data science, but they're a good start.
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Pandas is an open source, BSD-licensed library that makes it easy to process data structures for analysis. It depends on NumPy, a scientific library that provides multi-dimensional arrays, linear algebra, Fourier transforms, and much more. Install both using **pip3**:
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```
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`(example) $ pip3 install pandas`
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```
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Installing Pandas also installs NumPy, so you don't need to specify both. Once you have installed them to your virtual environment once, the installation packages are cached so that when you install them again, you don't have to download them from the internet.
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Those are the only libraries you need for now. Next, you need some sample data.
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### Generating a sample dataset
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Data science is all about data, and luckily there are lots of free and open datasets available from scientific, computing, and government organizations. While these datasets are a great resource for education, they have a lot more data than necessary for this simple example. You can create a sample and manageable dataset quickly with Python:
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```
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#!/usr/bin/env python3
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import random
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def rgb():
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NUMBER=random.randint(0,255)/255
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return NUMBER
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FILE = open('sample.csv','w')
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FILE.write('"red","green","blue"')
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for COUNT in range(10):
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FILE.write('\n{:0.2f},{:0.2f},{:0.2f}'.format(rgb(),rgb(),rgb()))
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```
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This produces a file called **sample.csv**, consisting of randomly generated floats representing, in this example, RGB values (a commonly tracked value, among hundreds, in visual effects). You can use a CSV file as a data source for Pandas.
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### Ingesting data with Pandas
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One of Pandas' basic features is its ability to ingest data and process it without the programmer writing new functions just to parse input. If you're used to applications that do that automatically, this might not seem like it's very special—but imagine opening a CSV in [LibreOffice][6] and having to write formulas to split the values at each comma. Pandas shields you from low-level operations like that. Here's some simple code to ingest and print out a file of comma-separated values:
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```
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#!/usr/bin/env python3
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from pandas import read_csv, DataFrame
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import pandas as pd
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FILE = open('sample.csv','r')
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DATAFRAME = pd.read_csv(FILE)
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print(DATAFRAME)
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```
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The first few lines import components of the Pandas library. The Pandas library is extensive, so you'll refer to its documentation frequently when looking for functions beyond the basic ones in this article.
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Next, a variable **f** is created by opening the **sample.csv** file you created. That variable is used by the Pandas module **read_csv** (imported in the second line) to create a _dataframe_. In Pandas, a dataframe is a two-dimensional array, commonly thought of as a table. Once your data is in a dataframe, you can manipulate it by column and row, query it for ranges, and do a lot more. The sample code, for now, just prints the dataframe to the terminal.
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Run the code. Your output will differ slightly from this sample output because the numbers are randomly generated, but the format is the same:
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```
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(example) $ python3 ./parse.py
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red green blue
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0 0.31 0.96 0.47
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1 0.95 0.17 0.64
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2 0.00 0.23 0.59
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3 0.22 0.16 0.42
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4 0.53 0.52 0.18
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5 0.76 0.80 0.28
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6 0.68 0.69 0.46
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7 0.75 0.52 0.27
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8 0.53 0.76 0.96
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9 0.01 0.81 0.79
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```
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Assume you need only the red values from your dataset. You can do this by declaring your dataframe's column names and selectively printing only the column you're interested in:
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```
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from pandas import read_csv, DataFrame
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import pandas as pd
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FILE = open('sample.csv','r')
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DATAFRAME = pd.read_csv(FILE)
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# define columns
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DATAFRAME.columns = [ 'red','green','blue' ]
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print(DATAFRAME['red'])
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```
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Run the code now, and you get just the red column:
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```
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(example) $ python3 ./parse.py
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0 0.31
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1 0.95
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2 0.00
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3 0.22
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4 0.53
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5 0.76
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6 0.68
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7 0.75
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8 0.53
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9 0.01
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Name: red, dtype: float64
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```
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Manipulating tables of data is a great way to get used to how data can be parsed with Pandas. There are many more ways to select data from a dataframe, and the more you experiment, the more natural it becomes.
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### Visualizing your data
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It's no secret that many humans prefer to visualize information. It's the reason charts and graphs are staples of meetings with upper management and why "infographics" are popular in the news business. Part of a data scientist's job is to help others understand large samples of data, and there are libraries to help with this task. Combining Pandas with a visualization library can produce visual interpretations of your data. One popular open source library for visualization is [Seaborn][7], which is based on the open source [Matplotlib][3].
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#### Installing Seaborn and Matplotlib
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Your Python virtual environment doesn't yet have Seaborn and Matplotlib, so install them with pip3. Seaborn also installs Matplotlib along with many other libraries:
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```
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`(example) $ pip3 install seaborn`
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```
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For Matplotlib to display graphics, you must also install [PyGObject][8] and [Pycairo][9]. This involves compiling code, which pip3 can do for you as long as you have the necessary header files and libraries installed. Your Python virtual environment has no awareness of these support libraries, so you can execute the installation command inside or outside the environment.
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On Fedora and CentOS:
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```
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(example) $ sudo dnf install -y gcc zlib-devel bzip2 bzip2-devel readline-devel \
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sqlite sqlite-devel openssl-devel tk-devel git python3-cairo-devel \
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cairo-gobject-devel gobject-introspection-devel
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```
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On Ubuntu and Debian:
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```
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(example) $ sudo apt install -y libgirepository1.0-dev build-essential \
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libbz2-dev libreadline-dev libssl-dev zlib1g-dev libsqlite3-dev wget \
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curl llvm libncurses5-dev libncursesw5-dev xz-utils tk-dev libcairo2-dev
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```
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Once they are installed, you can install the GUI components needed by Matplotlib:
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```
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`(example) $ pip3 install PyGObject pycairo`
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```
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### Displaying a graph with Seaborn and Matplotlib
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Open a file called **vizualize.py** in your favorite text editor. To create a line graph visualization of your data, first, you must import the necessary Python modules: the Pandas modules you used in the previous code examples:
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```
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#!/usr/bin/env python3
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from pandas import read_csv, DataFrame
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import pandas as pd
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```
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Next, import Seaborn, Matplotlib, and several components of Matplotlib so you can configure the graphics you produce:
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```
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import seaborn as sns
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import matplotlib
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import matplotlib.pyplot as plt
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from matplotlib import rcParams
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```
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Matplotlib can export its output to many formats, including PDF, SVG, or just a GUI window on your desktop. For this example, it makes sense to send your output to the desktop, so you must set the Matplotlib backend to GTK3Agg. If you're not using Linux, you may need to use the TkAgg backend instead.
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After setting the backend for the GUI window, set the size of the window and the Seaborn preset style:
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```
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matplotlib.use('GTK3Agg')
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rcParams['figure.figsize'] = 11,8
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sns.set_style('darkgrid')
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```
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Now that your display is configured, the code is familiar. Ingest your **sample.csv** file with Pandas, and define the columns of your dataframe:
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```
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FILE = open('sample.csv','r')
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DATAFRAME = pd.read_csv(FILE)
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DATAFRAME.columns = [ 'red','green','blue' ]
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```
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With the data in a useful format, you can plot it out in a graph. Use each column as input for a plot, then use **plt.show()** to draw the graph in a GUI window. The **plt.legend()** parameter associates the column header with each line on your graph (the **loc** parameter places the legend outside the chart rather than over it):
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```
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for i in DATAFRAME.columns:
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DATAFRAME[i].plot()
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plt.legend(bbox_to_anchor=(1, 1), loc=2, borderaxespad=1)
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plt.show()
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```
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Run the code to display the results.
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![First data visualization][10]
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Your graph accurately displays all the information contained in your CSV file: values are on the Y-axis, index numbers are on the X-axis, and the lines of the graph are identified so that you know what they represent. However, since this code is tracking color values (at least, it's pretending to), the colors of the lines are not just non-intuitive, but counterintuitive. If you never need to analyze color data, you may never run into this problem, but you're sure to run into something analogous. When visualizing data, you must consider the best way to present it to prevent the viewer from extrapolating false information from what you're presenting.
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To fix this problem (and show off some of the customization available), the following code assigns each plotted line a specific color:
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```
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import matplotlib
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from pandas import read_csv, DataFrame
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import pandas as pd
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import seaborn as sns
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import matplotlib.pyplot as plt
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from matplotlib import rcParams
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matplotlib.use('GTK3Agg')
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rcParams['figure.figsize'] = 11,8
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sns.set_style('whitegrid')
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FILE = open('sample.csv','r')
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DATAFRAME = pd.read_csv(FILE)
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DATAFRAME.columns = [ 'red','green','blue' ]
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plt.plot(DATAFRAME['red'],'r-')
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plt.plot(DATAFRAME['green'],'g-')
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plt.plot(DATAFRAME['blue'],'b-')
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plt.plot(DATAFRAME['red'],'ro')
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plt.plot(DATAFRAME['green'],'go')
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plt.plot(DATAFRAME['blue'],'bo')
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plt.show()
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```
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This uses special Matplotlib notation to create two plots per column. The initial plot of each column is assigned a color (**r** for red, **g** for green, and **b** for blue). These are built-in Matplotlib settings. The **-** notation indicates a solid line (a double dash, such as **r--**, creates a dashed line). A second plot is created for each column with the same colors but using **o** to denote dots or nodes. To demonstrate built-in Seaborn themes, change the value of **sns.set_style** to **whitegrid**.
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![Improved data visualization][11]
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### Deactivating your virtual environment
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When you're finished exploring Pandas and plotting, you can deactivate your Python virtual environment with the **deactivate** command:
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```
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(example) $ deactivate
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$
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```
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When you want to get back to it, just reactivate it as you did at the start of this article. You'll have to reinstall your modules when you reactivate your virtual environment, but they'll be installed from cache rather than downloaded from the internet, so you don't have to be online.
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### Endless possibilities
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The true power of Pandas, Matplotlib, Seaborn, and data science is the endless potential for you to parse, interpret, and structure data in a meaningful and enlightening way. Your next step is to explore simple datasets with the new tools you've learned in this article. There's a lot more to Matplotlib and Seaborn than just line graphs, so try creating a bar graph or a pie chart or something else entirely.
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The possibilities are limitless once you understand your toolset and have some idea of how to correlate your data. Data science is a new way to find stories hidden within data; let open source be your medium.
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Data visualization is the mechanism of taking tabular or spatial data and conveying it in a human-...
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--------------------------------------------------------------------------------
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via: https://opensource.com/article/19/9/get-started-data-science-python
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作者：[Seth Kenlon][a]
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选题：[lujun9972][b]
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译者：[译者ID](https://github.com/译者ID)
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校对：[校对者ID](https://github.com/校对者ID)
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|
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本文由 [LCTT](https://github.com/LCTT/TranslateProject) 原创编译，[Linux中国](https://linux.cn/) 荣誉推出
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[a]: https://opensource.com/users/sethhttps://opensource.com/users/jroakeshttps://opensource.com/users/tiwarinitish86
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[b]: https://github.com/lujun9972
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[1]: https://opensource.com/sites/default/files/styles/image-full-size/public/lead-images/metrics_graph_stats_blue.png?itok=OKCc_60D (Metrics and a graph illustration)
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[2]: https://pandas.pydata.org/
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[3]: https://matplotlib.org/
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[4]: https://seaborn.pydata.org/index.html
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[5]: https://opensource.com/article/17/10/python-101
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[6]: http://libreoffice.org
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[7]: https://seaborn.pydata.org/
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[8]: https://pygobject.readthedocs.io/en/latest/getting_started.html
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[9]: https://pycairo.readthedocs.io/en/latest/
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[10]: https://opensource.com/sites/default/files/uploads/seaborn-matplotlib-graph_0.png (First data visualization)
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[11]: https://opensource.com/sites/default/files/uploads/seaborn-matplotlib-graph_1.png (Improved data visualization)
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@ -0,0 +1,327 @@
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[#]: collector: (lujun9972)
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||||
[#]: translator: (GraveAccent)
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||||
[#]: reviewer: ( )
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||||
[#]: publisher: ( )
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[#]: url: ( )
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[#]: subject: (Getting started with data science using Python)
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[#]: via: (https://opensource.com/article/19/9/get-started-data-science-python)
|
||||
[#]: author: (Seth Kenlon https://opensource.com/users/sethhttps://opensource.com/users/jroakeshttps://opensource.com/users/tiwarinitish86)
|
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|
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用 Python 入门数据科学
|
||||
======
|
||||
使用 Python 开展数据科学为你以有意义和启发性的方式解析、解释和组织数据提供了无限的潜力。
|
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![指标和图标展示][1]
|
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|
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数据科学是计算领域一个令人兴奋的新领域，它围绕分析、可视化和关联以解释我们的计算机收集的有关世界的无限信息而建立。当然，称其为“新”领域有点不诚实，因为该学科是统计学，数据分析和普通老式强迫性科学观察的派生。
|
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|
||||
但是数据科学是这些学科的形式化分支，拥有自己的流程和工具，并且可以广泛应用于以前从未产生过大量不可管理数据的学科（例如视觉效果）。数据科学是一个新的机会，可以重新审视海洋学，气象学，地理学，制图学，生物学，医学和健康以及娱乐行业的数据，并更好地了解其中的模式、影响和因果关系。
|
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|
||||
像其他大型且看似包罗万象的领域一样，知道从哪里开始探索数据科学可能会令人生畏。有很多资源可以帮助数据科学家使用自己喜欢的编程语言来实现其目标，其中包括最流行的编程语言之一：Python。使用[Pandas][2]，[Matplotlib][3] 和 [Seaborn][4] 这些库，你可以学习数据科学的基本工具集。
|
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|
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如果你对 Python 的基本用法不是很熟悉，请在继续之前先阅读我的 [Python 介绍][5]。
|
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### 创建 Python 虚拟环境
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程序员有时会忘记在开发计算机上安装了哪些库，这可能导致他们提交在自己计算机上可以运行，但由于缺少库而无法在所有其它电脑上运行的代码。Python 有一个旨在避免这种令人不愉快的惊讶的系统：虚拟环境。虚拟环境会故意忽略你已安装的所有 Python库，从而有效地迫使你一开始使用现有的 Python 进行开发。
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||||
|
||||
为了用 **venv** 激活虚拟环境, 为你的环境取个名字 (我会用 **example**) 并且用下面的指令创建它:
|
||||
|
||||
```
|
||||
`$ python3 -m venv example`
|
||||
```
|
||||
|
||||
Source 环境的 **bin** 目录里的 **activate** 文件以激活它：
|
||||
|
||||
```
|
||||
$ source ./example/bin/activate
|
||||
(example) $
|
||||
```
|
||||
|
||||
你现在位于你的虚拟环境中。这是一个干净的状态，你可以在其中构建针对问题的自定义解决方案-额外增加了自觉需要安装依赖库的负担。
|
||||
|
||||
### 安装 Pandas 和 NumPy
|
||||
|
||||
您必须在新环境中安装的第一个库是 Pandas 和 NumPy。这些库在数据科学中很常见，因此这不是你最后一次安装它们。它们也不是你在数据科学中唯一需要的库，但是它们是一个好的开始。
|
||||
|
||||
Pandas 是使用 BSD 许可证的开源库，可轻松处理数据结构以进行分析。它依赖于 NumPy，这是一个提供多维数组，线性代数和傅立叶变换等等的科学库。使用 **pip3** 安装两者：
|
||||
|
||||
```
|
||||
`(example) $ pip3 install pandas`
|
||||
```
|
||||
|
||||
安装 Pandas 还会安装 NumPy，因此你无需同时指定两者。一旦将它们安装到虚拟环境中，安装包就会被缓存，这样，当你再次安装它们时，就不必从因特网下载它们。
|
||||
|
||||
这些是你现在仅需的库。接下来，你需要一些样本数据。
|
||||
|
||||
### 生成样本数据集
|
||||
|
||||
数据科学都是关于数据的，幸运的是，科学、计算和政府组织可以提供许多免费和开放的数据集。虽然这些数据集是用于教育的重要资源，但它们具有比此简单示例所需的数据更多的数据。你可以使用 Python 快速创建示例和可管理的数据集：
|
||||
|
||||
|
||||
```python
|
||||
#!/usr/bin/env python3
|
||||
|
||||
import random
|
||||
|
||||
def rgb():
|
||||
NUMBER=random.randint(0,255)/255
|
||||
return NUMBER
|
||||
|
||||
FILE = open('sample.csv','w')
|
||||
FILE.write('"red","green","blue"')
|
||||
for COUNT in range(10):
|
||||
FILE.write('\n{:0.2f},{:0.2f},{:0.2f}'.format(rgb(),rgb(),rgb()))
|
||||
```
|
||||
|
||||
这将生成一个名为 **sample.csv** 的文件，该文件由随机生成的浮点数组成，这些浮点数在本示例中表示 RGB 值（在视觉效果中通常是数百个跟踪值）。你可以将 CSV 文件用作 Pandas 的数据源。
|
||||
|
||||
### 使用 Pandas 提取数据
|
||||
|
||||
Pandas 的基本功能之一是可以提取数据和处理数据而无需程序员编写仅用于解析输入的新函数。如果你习惯于自动执行此操作的应用程序，那么这似乎不是很特别，但请想象一下在 LibreOffice[6] 中打开 CSV 并且必须编写公式以在每个逗号处拆分值。Pandas 保护你免受此类低级操作的影响。以下是一些简单的代码，可用于提取和打印以逗号分隔的值的文件：
|
||||
|
||||
|
||||
```python
|
||||
#!/usr/bin/env python3
|
||||
|
||||
from pandas import read_csv, DataFrame
|
||||
import pandas as pd
|
||||
|
||||
FILE = open('sample.csv','r')
|
||||
DATAFRAME = pd.read_csv(FILE)
|
||||
print(DATAFRAME)
|
||||
```
|
||||
|
||||
一开始的几行导入 Pandas 库的组件。Pandas 库内容广泛，因此在寻找除本文中基本功能以外的功能时，你会经常参考它的文档。
|
||||
|
||||
接下来，通过打开你创建的 **sample.csv** 文件创建变量 **FILE**。Pandas 模块 **read_csv**（在第二行中导入）使用该变量来创建 _dataframe_。在 Pandas 中，数据帧（dataframe）是二维数组，通常被认为是表格。数据放入数据帧中后，你可以按列和行进行操作，查询其范围，然后执行更多操作。目前，示例代码仅将数据帧输出到终端。
|
||||
|
||||
运行代码。你的输出会和下面的输出有些许不同，因为这些数字都是随机生成的，但是格式都是一样的。
|
||||
|
||||
|
||||
```
|
||||
(example) $ python3 ./parse.py
|
||||
red green blue
|
||||
0 0.31 0.96 0.47
|
||||
1 0.95 0.17 0.64
|
||||
2 0.00 0.23 0.59
|
||||
3 0.22 0.16 0.42
|
||||
4 0.53 0.52 0.18
|
||||
5 0.76 0.80 0.28
|
||||
6 0.68 0.69 0.46
|
||||
7 0.75 0.52 0.27
|
||||
8 0.53 0.76 0.96
|
||||
9 0.01 0.81 0.79
|
||||
```
|
||||
|
||||
假设你只需要数据集中的红色值，你可以通过声明数据帧的列名称并有选择地仅打印你感兴趣的列来做到这一点：
|
||||
|
||||
|
||||
```python
|
||||
from pandas import read_csv, DataFrame
|
||||
import pandas as pd
|
||||
|
||||
FILE = open('sample.csv','r')
|
||||
DATAFRAME = pd.read_csv(FILE)
|
||||
|
||||
# define columns
|
||||
DATAFRAME.columns = [ 'red','green','blue' ]
|
||||
|
||||
print(DATAFRAME['red'])
|
||||
```
|
||||
|
||||
现在运行代码，你只会得到红色列：
|
||||
|
||||
|
||||
```
|
||||
(example) $ python3 ./parse.py
|
||||
0 0.31
|
||||
1 0.95
|
||||
2 0.00
|
||||
3 0.22
|
||||
4 0.53
|
||||
5 0.76
|
||||
6 0.68
|
||||
7 0.75
|
||||
8 0.53
|
||||
9 0.01
|
||||
Name: red, dtype: float64
|
||||
```
|
||||
|
||||
处理数据表是习惯使用 Pandas 解析数据的好方法。从数据帧中选择数据的方法有很多，你尝试的次数越多就越自然。
|
||||
|
||||
### 可视化你的数据
|
||||
|
||||
很多人偏爱可视化信息已不是什么秘密，这是图表和图形成为与高层管理人员开会的主要内容的原因，也是“图表”在新闻界如此流行的原因。数据科学家的工作之一是帮助其他人理解大量数据样本，并且有一些库可以帮助你完成这项任务。将 Pandas 与可视化库结合使用可以对数据进行可视化解释。一个流行的可视化开源库是 [Seaborn][7]，它基于开源的 [Matplotlib][3]。
|
||||
|
||||
#### 安装 Seaborn 和 Matplotlib
|
||||
|
||||
你的 Python 虚拟环境还没有 Seaborn 和 Matplotlib，所以用 pip3 安装它们。安装 Seaborn 的时候，也会安装 Matplotlib 和很多其它的库。
|
||||
|
||||
|
||||
```
|
||||
`(example) $ pip3 install seaborn`
|
||||
```
|
||||
|
||||
为了使 Matplotlib 显示图形，你还必须安装 [PyGObject][8] 和 [Pycairo][9]。这涉及到编译代码，只要你安装了必需的头文件和库，pip3 便可以为你执行此操作。你的 Python 虚拟环境不了解这些依赖库，因此你可以在环境内部或外部执行安装命令。
|
||||
|
||||
在 Fedora 和 CentOS 上:
|
||||
|
||||
|
||||
```
|
||||
(example) $ sudo dnf install -y gcc zlib-devel bzip2 bzip2-devel readline-devel \
|
||||
sqlite sqlite-devel openssl-devel tk-devel git python3-cairo-devel \
|
||||
cairo-gobject-devel gobject-introspection-devel
|
||||
```
|
||||
|
||||
在 Ubuntu 和 Debian 上:
|
||||
|
||||
|
||||
```
|
||||
(example) $ sudo apt install -y libgirepository1.0-dev build-essential \
|
||||
libbz2-dev libreadline-dev libssl-dev zlib1g-dev libsqlite3-dev wget \
|
||||
curl llvm libncurses5-dev libncursesw5-dev xz-utils tk-dev libcairo2-dev
|
||||
```
|
||||
|
||||
一旦它们安装好了，你可以安装 Matplotlib 需要的 GUI 组件。
|
||||
|
||||
|
||||
```
|
||||
`(example) $ pip3 install PyGObject pycairo`
|
||||
```
|
||||
|
||||
### 用 Seaborn 和 Matplotlib 显示图形
|
||||
|
||||
在你最喜欢的文本编辑器打开一个叫 **vizualize.py** 的文件。要创建数据的线形图可视化，首先，你必须导入必要的 Python 模块：先前代码示例中使用的 Pandas 模块：
|
||||
|
||||
|
||||
```python
|
||||
#!/usr/bin/env python3
|
||||
|
||||
from pandas import read_csv, DataFrame
|
||||
import pandas as pd
|
||||
```
|
||||
|
||||
接下来，导入 Seaborn，Matplotlib 和 Matplotlib 的几个组件，以便你可以配置生成的图形：
|
||||
|
||||
|
||||
```python
|
||||
import seaborn as sns
|
||||
import matplotlib
|
||||
import matplotlib.pyplot as plt
|
||||
from matplotlib import rcParams
|
||||
```
|
||||
|
||||
Matplotlib 可以将其输出导出为多种格式，包括 PDF，SVG 和桌面上的 GUI 窗口。对于此示例，将输出发送到桌面很有意义，因此必须将 Matplotlib 后端设置为 GTK3Agg。如果你不使用 Linux，则可能需要使用 TkAgg 后端。
|
||||
|
||||
设置完 GUI 窗口以后，设置窗口大小和 Seaborn 预设样式：
|
||||
|
||||
|
||||
```python
|
||||
matplotlib.use('GTK3Agg')
|
||||
rcParams['figure.figsize'] = 11,8
|
||||
sns.set_style('darkgrid')
|
||||
```
|
||||
|
||||
现在，你的显示已配置完毕，代码已经很熟悉了。使用 Pandas 导入 **sample.csv** 文件，并定义数据帧的列：
|
||||
|
||||
|
||||
```python
|
||||
FILE = open('sample.csv','r')
|
||||
DATAFRAME = pd.read_csv(FILE)
|
||||
DATAFRAME.columns = [ 'red','green','blue' ]
|
||||
```
|
||||
|
||||
有了适当格式的数据，你可以将其绘制在图形中。将每一列用作绘图的输入，然后使用 **plt.show()** 在 GUI 窗口中绘制图形。**plt.legend()** 参数将列标题与图形上的每一行关联（**loc** 参数将图例放置在图表之外而不是在图表上方）：
|
||||
|
||||
|
||||
```python
|
||||
for i in DATAFRAME.columns:
|
||||
DATAFRAME[i].plot()
|
||||
|
||||
plt.legend(bbox_to_anchor=(1, 1), loc=2, borderaxespad=1)
|
||||
plt.show()
|
||||
```
|
||||
|
||||
运行代码以获得结果。
|
||||
|
||||
![第一个数据可视化][10]
|
||||
|
||||
你的图形可以准确显示 CSV 文件中包含的所有信息：值在 Y 轴上，索引号在 X 轴上，并且可以识别图形中的线，以便你知道它们代表什么。然而，由于此代码正在跟踪颜色值（至少是假装），所以线条的颜色不仅不直观，而且违反直觉。如果你永远不需要分析颜色数据，则可能永远不会遇到此问题，但是你一定会遇到类似的问题。在可视化数据时，你必须考虑呈现数据的最佳方法，以防止观看者从你呈现的内容中推断出虚假信息。
|
||||
|
||||
为了解决此问题（并展示一些可用的自定义设置），以下代码为每条绘制的线分配了特定的颜色：
|
||||
|
||||
|
||||
```python
|
||||
import matplotlib
|
||||
from pandas import read_csv, DataFrame
|
||||
import pandas as pd
|
||||
import seaborn as sns
|
||||
import matplotlib.pyplot as plt
|
||||
from matplotlib import rcParams
|
||||
|
||||
matplotlib.use('GTK3Agg')
|
||||
rcParams['figure.figsize'] = 11,8
|
||||
sns.set_style('whitegrid')
|
||||
|
||||
FILE = open('sample.csv','r')
|
||||
DATAFRAME = pd.read_csv(FILE)
|
||||
DATAFRAME.columns = [ 'red','green','blue' ]
|
||||
|
||||
plt.plot(DATAFRAME['red'],'r-')
|
||||
plt.plot(DATAFRAME['green'],'g-')
|
||||
plt.plot(DATAFRAME['blue'],'b-')
|
||||
plt.plot(DATAFRAME['red'],'ro')
|
||||
plt.plot(DATAFRAME['green'],'go')
|
||||
plt.plot(DATAFRAME['blue'],'bo')
|
||||
|
||||
plt.show()
|
||||
```
|
||||
|
||||
这使用特殊的 Matplotlib 表示法为每列创建两个图。每列的初始图分配有一种颜色（红色为 **r**，绿色为 **g**，蓝色为 **b**）。这些是内置的 Matplotlib 设置。 **-** 表示实线（双破折号，例如**r-**，将创建虚线）。为每个具有相同颜色的列创建第二个图，但是使用 **o** 表示点或节点。为了演示内置的 Seaborn 主题，请将 **sns.set_style** 的值更改为 **whitegrid**。
|
||||
|
||||
![改进的数据可视化][11]
|
||||
|
||||
### 停用你的虚拟环境
|
||||
|
||||
探索完 Pandas 和绘图后，可以使用 **deactivate** 命令停用 Python 虚拟环境：
|
||||
|
||||
|
||||
```
|
||||
(example) $ deactivate
|
||||
$
|
||||
```
|
||||
|
||||
当你想重新使用它时，只需像在本文开始时一样重新激活它即可。重新激活虚拟环境时，你必须重新安装模块，但是它们是从缓存安装的，而不是从因特网下载的，因此你不必在线。
|
||||
|
||||
### 无尽的可能性
|
||||
|
||||
Pandas，Matplotlib，Seaborn 和数据科学的真正力量是无穷的潜力，使你能够以有意义和启发性的方式解析，解释和组织数据。下一步是使用你在本文中学到的新工具探索简单的数据集。Matplotlib 和 Seaborn 不仅有折线图，还有很多其他功能，因此，请尝试创建条形图或饼图或完全不一样的东西。
|
||||
|
||||
一旦你了解了你的工具集并对如何关联数据有了一些想法，则可能性是无限的。数据科学是寻找隐藏在数据中的故事的新方法。让开源成为你的媒体。
|
||||
|
||||
数据可视化是一种获取表格或空间数据并传达给人类的方式-...
|
||||
|
||||
--------------------------------------------------------------------------------
|
||||
|
||||
via: https://opensource.com/article/19/9/get-started-data-science-python
|
||||
|
||||
作者：[Seth Kenlon][a]
|
||||
选题：[lujun9972][b]
|
||||
译者：[GraveAccent](https://github.com/graveaccent)
|
||||
校对：[校对者ID](https://github.com/校对者ID)
|
||||
|
||||
本文由 [LCTT](https://github.com/LCTT/TranslateProject) 原创编译，[Linux中国](https://linux.cn/) 荣誉推出
|
||||
|
||||
[a]: https://opensource.com/users/sethhttps://opensource.com/users/jroakeshttps://opensource.com/users/tiwarinitish86
|
||||
[b]: https://github.com/lujun9972
|
||||
[1]: https://opensource.com/sites/default/files/styles/image-full-size/public/lead-images/metrics_graph_stats_blue.png?itok=OKCc_60D (Metrics and a graph illustration)
|
||||
[2]: https://pandas.pydata.org/
|
||||
[3]: https://matplotlib.org/
|
||||
[4]: https://seaborn.pydata.org/index.html
|
||||
[5]: https://opensource.com/article/17/10/python-101
|
||||
[6]: http://libreoffice.org
|
||||
[7]: https://seaborn.pydata.org/
|
||||
[8]: https://pygobject.readthedocs.io/en/latest/getting_started.html
|
||||
[9]: https://pycairo.readthedocs.io/en/latest/
|
||||
[10]: https://opensource.com/sites/default/files/uploads/seaborn-matplotlib-graph_0.png (First data visualization)
|
||||
[11]: https://opensource.com/sites/default/files/uploads/seaborn-matplotlib-graph_1.png (Improved data visualization)
|
||||
Loading…
Reference in New Issue
Block a user