6 Python office hacks to increase work efficiency by 100 times! (With code) python black technology

Preface

Getting off work late and working overtime for long periods of time have become common problems among modern workers. They waste most of their time on mechanical and repetitive tasks every day. How to improve your own work efficiency is the key.

Today I will share with you 6 Python office tips to double your work efficiency.You are welcome to learn, collect, like and support.

Without further ado, let’s start learning.

1. Parse PDF (resume recommendation)

Application scenario: Resume recommendation (parsed content: including but not limited to name, email, phone number, education and other information)

Input: File path to parse

Output: Content to be parsed

Environment preparation:python 3.6, mac (The conversion of doc to docx below is the mac writing method. Windows is simpler, just import the win32 package. )

Dependency packages:

from pdfminer.pdfparser import PDFParser

from pdfminer.pdfdocument import PDFDocument

from pdfminer.pdfpage import PDFPage

from pdfminer.pdfinterp import PDFResourceManager

from pdfminer.pdfinterp import PDFPageInterpreter

from pdfminer.layout import LAParams

from pdfminer.converter import PDFPageAggregator
 
def pdf_reader(file):
    fp = open(file, "rb")
    # 创建一个与文档相关联的解释器
    parser = PDFParser(fp)
    # PDF文档对象
    doc = PDFDocument(parser)
    # 链接解释器和文档对象
    parser.set_document(doc)
    # doc.set_paeser(parser)
    # 初始化文档
    # doc.initialize("")
    # 创建PDF资源管理器
    resource = PDFResourceManager()
    # 参数分析器
    laparam = LAParams()
    # 创建一个聚合器
    device = PDFPageAggregator(resource, laparams=laparam)
    # 创建PDF页面解释器
    interpreter = PDFPageInterpreter(resource, device)
    # 使用文档对象得到页面集合
    res = ''
    for page in PDFPage.create_pages(doc):
        # 使用页面解释器来读取
        interpreter.process_page(page)
        # 使用聚合器来获取内容
        layout = device.get_result()
        for out in layout:
            if hasattr(out, "get_text"):
                res = res + '' + out.get_text()
    return res

2. Send email

There are several modules for accessing the Internet and handling network communication protocols. The two simplest of these are urllib.request for handling data received from urls and smtplib for sending emails:

smtpObj = smtplib.SMTP( [host [, port [, local_hostname]]] )

Numerical explanation：

• host: SMTP server host. You can specify the IP address or domain name of the host such as: http://runoob.com. This is an optional parameter.
• port: If you provide the host parameter, you need to specify the port number used by the SMTP service. Generally, the SMTP port number is 25.
• local_hostname: If SMTP is on your local machine, you only need to specify the server address as localhost.

The Python SMTP object uses the sendmail method to send emails. The syntax is as follows:

SMTP.sendmail(from_addr, to_addrs, msg[, mail_options, rcpt_options])

Numerical explanation：

• from_addr: Email sender address.
• to_addrs: string list, email sending address.
• msg: send message

Case:

from email.mime.text import MIMEText

from email.header import Header

sender = '[email protected]'

receivers = ['[email protected]']  # 接收邮件，可设置为你的QQ邮箱或者其他邮箱

# 三个参数：第一个为文本内容，第二个 plain 设置文本格式，第三个 utf-8 设置编码

message = MIMEText('Python 邮件发送测试...', 'plain', 'utf-8')

message['From'] = Header("不吃西红柿", 'utf-8')   # 发送者

message['To'] =  Header("测试", 'utf-8')        # 接收者

subject = 'Python SMTP 邮件测试'

message['Subject'] = Header(subject, 'utf-8')

    smtpObj = smtplib.SMTP('localhost')

    smtpObj.sendmail(sender, receivers, message.as_string())

except smtplib.SMTPException:

    print "Error: 无法发送邮件"

3. Operation execl

1. Related formula: Vlookup

vlookup is almost the most commonly used formula in Excel, and is generally used for related queries between two tables. So I first divided this table into two tables.

df1["订单明细号"].duplicated().value_counts()

df2["订单明细号"].duplicated().value_counts()

df_c=pd.merge(df1,df2,on="订单明细号",how="left")

2. Pivot table

Requirement: I want to know the total profit and average profit earned by salespersons in each region.

pd.pivot_table(sale,index="地区名称",columns="业务员名称",values="利润",aggfunc=[np.sum,np.mean])

3. Compare the difference between two columns

Requirement: Compare the difference between the order detail number and order detail number 2 and display it.

sale["订单明细号2"]=sale["订单明细号"]

sale["订单明细号2"][1:10]=sale["订单明细号2"][1:10]+1

result=sale.loc[sale["订单明细号"].isin(sale["订单明细号2"])==False]

4. Remove duplicate values

Requirement: Remove duplicate values ​​of salesperson codes

sale.drop_duplicates("业务员编码",inplace=True)

5. Missing value processing

sale["客户名称"]=sale["客户名称"].fillna(0)

sale.dropna(subset=["客户编码"])

6. Multi-condition filtering

Demand: I want to know the information about salesperson Zhang Ai, who sells goods in the Beijing area with an order amount greater than 6,000.

sale.loc[(sale["地区名称"]=="北京")&(sale["业务员名称"]=="张爱")&(sale["订单金额"]>5000)]

7. Fuzzy filtering of data

Requirement: Filter information whose inventory name contains "Samsung" or "Sony".

sale.loc[sale["存货名称"].str.contains("三星|索尼")]

8. Classification and summary

Demand: The total profit of each salesperson in the Beijing area.

sale.groupby(["地区名称","业务员名称"])["利润"].sum()

9. Conditional calculation

Demand: How many orders have the inventory name containing "Samsung" and a tax higher than 1,000? What is the sum and average profit of these orders? (or minimum value, maximum value, quartile, label difference)

sale.loc[sale["存货名称"].str.contains("三星")&(sale["税费"]>=1000)][["订单明细号","利润"]].describe()

10. Remove spaces between data

Requirement: Remove the spaces on both sides of the inventory name.

sale["存货名称"].map(lambda s :s.strip(""))

4. Drawing analysis

League of Legends defense:

The hero with the lowest defense ability (level 1): Night Hunter, Magic Cat, All Flowers Psychic The hero with the highest defense ability (level 10): Colossus of Justice, Armored Dragon Turtle

Annie and Kalma ability matrix:

Code example:

from pyecharts.charts import Pie

from pyecharts import options as opts

from pyecharts.charts import Radar

    from pyecharts.charts import Radar

    # //由于雷达图传入的数据得为多维数据，所以这里需要做一下处理

    radar_data = [[10, 10, 10, 10, 10]]

    radar_data1 = [[2, 10, 3, 6, 3]]

    radar_data2 = [[1, 8, 7, 5, 8]]

    # //设置column的最大值，为了雷达图更为直观，这里的月份最大值设置有所不同

        ("物理", 100), ("魔法", 10), ("防御", 10),("难度", 10),("喜好", 10)

    radar.add_schema(schema)

    radar.add("满分", radar_data)

    # //一般默认为同一种颜色，这里为了便于区分，需要设置item的颜色

    radar.add("安妮", radar_data1, color="#E37911")

    radar.add("卡尔玛", radar_data2, color="#1C86EE")

if __name__ == '__main__':

5. Parse word (docx, doc)

Dependency packages:

        if 'docx' in file:

            f = docx.Document(file)

            for para in f.paragraphs:

                res = res + '\n' +para.text

            # 先转格式doc>docx

            os.system("textutil -convert docx '%s'"%file)

            word_reader(file+'x')

            f = docx.Document(file+'x')

            for para in f.paragraphs:

                res = res + '\n' +para.text

        # print(file, 'read failed')

6. Calculator

The math module provides access to the underlying function library for floating point operations:

>>> math.cos(math.pi / 4)

Recommended learning resources

In addition to the above sharing, if you also like programming and want to get a higher salary by learning Python, here is a Python learning material to share with you.

-END-

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