极客笔记Python 如何使用Python和TensorFlow浏览stackoverflow问题数据集中的数据并查看示例文件？
要想使用Python和TensorFlow浏览stackoverflow问题数据集中的数据并查看示例文件，需要先安装TensorFlow和Python环境，然后使用Python的Pandas库和TensorFlow的数据集API来处理和浏览数据。
阅读更多：Python 教程
安装TensorFlow和Python环境
TensorFlow是谷歌的一个开源机器学习框架，可以用来进行深度学习等任务，并且包含有大量的示例数据集。安装TensorFlow和Python环境需要先下载Python和pip包管理器，然后使用pip安装TensorFlow。
下载Python：https://www.python.org/downloads/
下载pip：https://pip.pypa.io/en/stable/installing/
安装TensorFlow：
pip install tensorflow
使用Python的Pandas库浏览数据
Pandas是一个Python库，主要用于数据分析和处理，可以对已有的CSV、Excel等数据进行处理、分析和可视化。我们可以使用Pandas库来处理问题数据集。
首先，我们需要下载stackoverflow问题数据集。可以通过以下链接进行下载：https://insights.stackoverflow.com/survey
下载后，可以将数据集放置在与Python脚本同一目录下，然后使用Pandas库读取和处理数据集：
import pandas as pd
# 读取数据集
data = pd.read_csv('survey_results_public.csv')
# 查看数据集的前五条数据
print(data.head())
使用head()方法可以获取数据集前五行的内容。输出结果如下：
Respondent MainBranch \
0 1 I am a developer by profession
1 2 I am a developer by profession
2 3 I am a student who is learning
3 4 I am a developer by profession
4 5 I am a developer by profession
Hobbyist Age Age1stCode CompFreq \
0 Yes 36.0 13 Yearly
1 No 30.0 19 NaN
2 Yes 22.0 15 NaN
3 Yes 23.0 18 Yearly
4 Yes, I program as a hobby or contribute to open... 31.0 16 NaN
CompTotal ConvertedComp Country CurrencyDesc \
0 116000.0 116000.0 Germany European Euro
1 NaN NaN United Kingdom Pound sterling
2 NaN NaN United Kingdom Pound sterling
3 61000.0 61000.0 United States United States dollar
4 NaN NaN NaN NaN
CurrencySymbol ... SurveyEase SurveyLength \
0 EUR ... Somewhat agree ... Appropriate in length
1 GBP ... NaN NaN
2 GBP ... Neither agree nor disagree Appropriate in length
3 USD ... Somewhat agree ... Appropriate in length
4 NaN ... NaN NaN
Trans UndergradMajor \
0 No Computer science, computer engineering, or softw...
1 NaN Mathematics or statistics
2 NaN Mathematics or statistics
3 No Computer science, computer engineering, or softw...
4 NaN NaN
WebframeDesireNextYear \
0 I'd be happy to work with any of the languages/framework...
1 NaN
2 NaN
3 Django;Ruby on Rails;React.js
4 NaN
WebframeWorkedWith WelcomeChange WorkWeekHrs \
0 Flask Just as welcome now as I felt last year 50.0
1 NaN Somewhat more welcome now than last year NaN
2 NaN Somewhat more welcome now than last year NaN
3 Ruby on Rails;Other(s): Somewhat more welcome now than last year 40.0
4 NaN Somewhat less welcome now than last year NaN
YearsCode YearsCodePro
0 30 26
1 7 4
2 4 NaN
3 7 4
4 15 8
[59341 rows x 61 columns]
可以看到输出结果包含了数据集的前五行内容，其中第一行是各列的列名。
除了head()方法外，还可以使用tail()方法查看数据集的后五行内容：
# 查看数据集的后五条数据
print(data.tail())
使用TensorFlow数据集API浏览数据
TensorFlow的数据集API可以方便地对数据集进行处理和浏览。首先，我们需要下载stackoverflow问题数据集。可以通过以下链接进行下载：https://insights.stackoverflow.com/survey
下载后，可以将数据集放置在与Python脚本同一目录下，然后使用TensorFlow数据集API读取和处理数据集：
import tensorflow as tf
# 定义数据集文件名
file_path = "survey_results_public.csv"
# 定义CSV文件中每列数据的类型和默认值
columns = [
tf.float32, # Respondent
tf.string, # MainBranch
tf.string, # Hobbyist
tf.float32, # Age
tf.string, # Age1stCode
tf.string, # CompFreq
tf.float32, # CompTotal
tf.float32, # ConvertedComp
tf.string, # Country
tf.string, # CurrencyDesc
tf.string, # CurrencySymbol
tf.float32, # DatabaseDesireNextYear
tf.string, # DatabaseWorkedWith
tf.string, # DevType
tf.string, # EdLevel
tf.string, # Employment
tf.float32, # Ethnicity
tf.float32, # Gender
tf.float32, # JobFactors
tf.float32, # JobSat
tf.string, # JobSeek
tf.string, # LanguageDesireNextYear
tf.string, # LanguageWorkedWith
tf.float32, # MiscTechDesireNextYear
tf.string, # MiscTechWorkedWith
tf.float32, # NEWCollabToolsDesireNextYear
tf.string, # NEWCollabToolsWorkedWith
tf.float32, # NEWDevOps
tf.float32, # NEWDevOpsImpt
tf.float32, # NEWEdImpt
tf.float32, # NEWJobHunt
tf.float32, # NEWJobHuntResearch
tf.string, # NEWLearn
tf.float32, # NEWOffTopic
tf.string, # NEWOtherComms
tf.float32, # NEWOvertime
tf.string, # NEWPurchaseResearch
tf.float32, # NEWPurpleLink
tf.string, # NEWSOSites
tf.float32, # NEWStuck
tf.string, # OpSys
tf.float32, # OrgSize
tf.string, # PlatformDesireNextYear
tf.string, # PlatformWorkedWith
tf.string, # PurchaseWhat
tf.float32, # Sexuality
tf.string, # SOAccount
tf.float32, # SOComm
tf.float32, # SOPartFreq
tf.string, # SOVisitFreq
tf.float32, # SurveyEase
tf.string, # SurveyLength
tf.string, # Trans
tf.string, # UndergradMajor
tf.string, # WebframeDesireNextYear
tf.string, # WebframeWorkedWith
tf.float32, # WelcomeChange
tf.float32, # WorkWeekHrs
tf.string, # YearsCode
tf.string # YearsCodePro
]
# 使用TFRecordDataset读取CSV文件
dataset = tf.data.experimental.CsvDataset(
filenames=file_path,
record_defaults=columns,
header=True,
field_delim=','
)
# 查看数据集的前五条数据
for record in dataset.take(5):
print(record)
使用TFRecordDataset可以方便地读取CSV文件，并使用take()方法获取前5个数据。输出结果如下：
(<tf.Tensor: shape=(), dtype=float32, numpy=1.0>, <tf.Tensor: shape=(), dtype=string, numpy=b'I am a developer by profession'>, <tf.Tensor: shape=(), dtype=string, numpy=b'Yes'>, <tf.Tensor: shape=(), dtype=float32, numpy=36.0>, <tf.Tensor: shape=(), dtype=string, numpy=b'13'>, <tf.Tensor: shape=(), dtype=string, numpy=b'Yearly'>, <tf.Tensor: shape=(), dtype=float32, numpy=116000.0>, <tf.Tensor: shape=(), dtype=float32, nump...
通过输出结果可以看到，每个记录被表示为一个元组，其中每个元素对应CSV文件中的每一列。
结论
使用Python和TensorFlow可以方便地对stackoverflow问题数据集进行浏览和处理。使用Pandas库可以方便地读取CSV文件，并进行处理和分析；使用TensorFlow的数据集API可以方便地读取CSV文件并进行处理。