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Python数据分析入门：实战指南

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"《用Python学习数据分析：编码课程》是一本旨在帮助读者掌握数据分析技能的实用指南，由A.J. Henley和Dave Wolf合著。该书特别针对Python编程语言，适合初学者和有一定基础的用户，无论他们是否已熟悉Python。它不仅为那些已经使用Python进行数据分析的用户提供深入操作技巧，还为新接触此领域的读者提供了基础知识的入门教程。书中结构紧凑，包含三个实际练习和一个案例研究，这些内容均围绕正确格式的数据输入和输出Python代码展开。作者力求每节课独立且完整，以便读者能够灵活地按照个人需求和进度学习。通过本书，读者不仅能学习数据分析的基本概念和技术，还能学习如何将分析结果可视化，以便更好地理解和解释数据背后的含义。对于那些已经具备Python基础的读者，这本书将加深他们对数据分析工具和方法的理解，帮助他们提升专业技能并将其应用于实际项目。而对于初次接触Python和数据分析的读者，本书则是一个循序渐进的学习平台，为他们打下坚实的理论基础和实践能力。《用Python学习数据分析：编码课程》的纸质版ISBN-13为978-1-4842-3485-3，电子版为978-1-4842-3486-0，可通过DOI:10.1007/978-1-4842-3486-0获取。此外，该书还受到版权保护，所有权利归出版社所有，包括翻译、复制、重印、再现、广播、微缩胶片制作、物理或数字形式的传播等。书中可能会出现商标名、标志和图像，为了尊重知识产权，作者并未在每次提及时都使用商标符号，而是直接使用名称、标志和图像。这是一本既实用又全面的资源，无论是数据分析师的专业成长，还是对Python数据分析感兴趣的业余爱好者，都是值得投资的宝贵参考资料。"

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Your Turn

Can you make a dataframe from a file you have uploaded and imported

on your own? Let's find out. Go to the following website, which contains

U.S.Census data (http://census.ire.org/data/bulkdata.html), and

download the CSV datafile for a state. Now, try to import that data into

Python.

Saving Data toCSV

Maybe you want to save your progress when analyzing data. Maybe you are

just using Python to massage some data for later analysis in another tool.

Or maybe you have some other reason to export your dataframe to a CSV

file. The code shown in Listing 2-6 is an example of how to do this.

Listing 2-6. Exporting a Dataset to CSV

import pandas as pd

names = ['Bob','Jessica','Mary','John','Mel']

grades = [76,95,77,78,99]

GradeList = zip(names,grades)

df = pd.DataFrame(data = GradeList, columns=['Names','Grades'])

df.to_csv('studentgrades.csv',index=False,header=False)

Lines 1 to 6 are the lines that create the dataframe. Line 7 is the code to

export the dataframe df to a CSV file called studentgrades.csv.

The only parameters we use are index and header. Setting these

parameters to false will prevent the index and header names from

being exported. Change the values of these parameters to get a better

understanding of their use.

CHAPTER 2 GETTING DATA INTO ANDOUT OFPYTHON

If you want in-depth information about the to_csv method, you can, of

course, use the code shown in Listing 2-7.

Listing 2-7. Getting Help on to_csv

df.to_csv?

Your Turn

Can you export the dataframe created by the code in Listing 2-8 to CSV?

Listing 2-8. Creating a Dataset for the Exercise

import pandas as pd

names = ['Bob','Jessica','Mary','John','Mel']

grades = [76,95,77,78,99]

bsdegrees = [1,1,0,0,1]

msdegrees = [2,1,0,0,0]

phddegrees = [0,1,0,0,0]

Degrees = zip(names,grades,bsdegrees,msdegrees,phddegrees)

columns = ['Names','Grades','BS','MS','PhD']

df = pd.DataFrame(data = Degrees, columns=column)

Loading Data fromExcel Files

Normally, data will come to us as files or database links. Let's see how to

load data from an Excel file (Listing 2-9).

CHAPTER 2 GETTING DATA INTO ANDOUT OFPYTHON

Listing 2-9. Loading Data from Excel File

import pandas as pd

Location = "datasets/gradedata.xlsx"

df = pd.read_excel(Location)

Now, let's take a look at what our data looks like (Listing 2-10).

Listing 2-10. Display First Five Lines of Data

df.head()

If you wish to change or simplify your column names, you can run the

code shown in Listing 2-11.

Listing 2-11. Changing Column Names

df.columns = ['first','last','sex','age','exer','hrs','grd','addr']

df.head()

Your Turn

Can you make a dataframe from a file you have uploaded and imported

on your own? Let's find out. Go to https://www.census.gov/support/

USACdataDownloads.html and download one of the Excel datafiles at the

bottom of the page. Now, try to import that data into Python.

CHAPTER 2 GETTING DATA INTO ANDOUT OFPYTHON

Saving Data toExcel Files

The code shown in Listing 2-12 is an example of how to do this.

Listing 2-12. Exporting a Dataframe to Excel

import pandas as pd

names = ['Bob','Jessica','Mary','John','Mel']

grades = [76,95,77,78,99]

GradeList = zip(names,grades)

df = pd.DataFrame(data = GradeList,

columns=['Names','Grades'])

writer = pd.ExcelWriter('dataframe.xlsx', engine='xlsxwriter')

df.to_excel(writer, sheet_name='Sheet1')

writer.save()

If you wish, you can save different dataframes to different sheets, and

with one .save() you will create an Excel file with multiple worksheets

(see Listing 2-13).

Listing 2-13. Exporting Multiple Dataframes to Excel

writer = pd.ExcelWriter('dataframe.xlsx',engine='xlsxwriter')

df.to_excel(writer, sheet_name='Sheet1')

df2.to_excel(writer, sheet_name='Sheet2')

writer.save()

Note This assumes that you have another dataframe already

loaded into the df2 variable.

CHAPTER 2 GETTING DATA INTO ANDOUT OFPYTHON

Your Turn

Can you export the dataframe created by the code shown in Listing 2-14 to

Excel?

Listing 2-14. Creating a Dataset for the Exercise

import pandas as pd

names = ['Nike','Adidas','New Balance','Puma',’Reebok’]

grades = [176,59,47,38,99]

PriceList = zip(names,prices)

df = pd.DataFrame(data = PriceList, columns=['Names',’Prices’])

Combining Data fromMultiple Excel Files

In earlier lessons, we opened single files and put their data into individual

dataframes. Sometimes we will need to combine the data from several

Excel files into the same dataframe.

We can do this either the long way or the short way. First, let's see the

long way (Listing 2-15).

Listing 2-15. Long Way

import pandas as pd

import numpy as np

all_data = pd.DataFrame()

df = pd.read_excel("datasets/data1.xlsx")

all_data = all_data.append(df,ignore_index=True)

df = pd.read_excel("datasets/data2.xlsx")

all_data = all_data.append(df,ignore_index=True)

CHAPTER 2 GETTING DATA INTO ANDOUT OFPYTHON

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