数据导入：
导入csvfrom pandas import read_csv;df = read_csv('D://PA//4.1//1.csv')导入文本，要转成UTF-8无BOM格式：from pandas import read_table;df = read_table('D://PA//4.1//2.txt')导入exclefrom pandas import read_excel;df = read_excel('C:/PA/4.1/3.xlsx') 数据导出：
数据的导出：
from pandas import DataFrame;df = DataFrame({ 'age': [21, 22, 23], 'name': ['KEN', 'John', 'JIMI']});df.to_csv("c:/PA/4.1/df.csv");#不导入序号df.to_csv("c:/PA/4.1/df.csv", index=False); 重复值处理：
from pandas import read_csv;df = read_csv('C:/PA/4.1/data.csv')newDF = df.drop_duplicates(); 缺失值处理：
from pandas import read_csv;df = read_csv('C:/PA/4.4/data.csv')newDF = df.dropna(); 空格值处理：
from pandas import read_csv;df = read_csv('C:/PA/4.5/data.csv')newDF = df["name"].str.strip();df["name"]=newDF; 字段抽取：
astype(str) 转换成字符型数据，以便于处理。
from pandas import read_csv;df = read_csv('C:/PA/4.6/data.csv')df["tel"]=df["tel"].astype(str);bands=df["tel"].str.slice(0,3);areas=df["tel"].str.slice(3,7);numbs=df["tel"].str.slice(7,11); 字段拆分：
from pandas import read_csv;df=read_csv("C:/PA/4.7/data.csv");newDF=df["name"].str.split(" ",1,True);newDF.columns=["band","name"]; 记录抽取：
import pandas;from pandas import read_csv;df=read_csv("C:/PA/4.8/data.csv",sep="|");df[df.comments>1000];df[df.comments.between(1000,10000)];df[pandas.isnull(df.title)];df[df.title.str.contains("台电",na=False)];df[(df.comments>=1000)&(df.comments<=10000)] 随机抽样：
import numpy;from pandas import read_csv;df=read_csv("C:/PA/4.9/data.csv");r=numpy.random.randint(0,10,3);df.loc[r,:]; 记录合并 ：
import pandas;from pandas import read_csv;df1=read_csv("C:/PA/4.10/data1.csv",sep="|");df2=read_csv("C:/PA/4.10/data2.csv",sep="|");df3=read_csv("C:/PA/4.10/data3.csv",sep="|");df=pandas.concat([df1,df2,df3]) 字段合并：
from pandas import read_csv;df = read_csv( "C:/PA/4.11/data.csv", sep=" ", names=['band', 'area', 'num']);df = df.astype(str);tel = df['band'] + df['area'] + df['num']
字段匹配：
import pandas;from pandas import read_csv;item=read_csv( "C:/PA/4.12/data1.csv", sep="|", names=["id","comments","title"]);prices=read_csv( "C:/PA/4.12/data1.csv", sep="|", names=["id","oldprice","newprice"] )itemprices=pandas.merge( item, prices, left_on="id", right_on="id" );
简单计算：
import pandas;from pandas import read_csv;df=read_csv("C:/PA/4.13/data.csv",sep="|");result=df.price*df.numdf["sum"]=result 数据标准化：
import pandas;from pandas import read_csv;df=read_csv("C:/PA/4.14/data.csv");scale=(df.score-df.score.min())/(df.score.max()-df.score.min()) 数据分组：
import pandas;from pandas import read_csv;df = read_csv("C:\\PA\\4.15\\data.csv", sep='|');bins = [min(df.cost)-1, 20, 40, 60, 80, 100, max(df.cost)+1];labels = ['20以下', '20到40', '40到60', '60到80', '80到100', '100以上'];pandas.cut(df.cost, bins)pandas.cut(df.cost, bins, right=False)pandas.cut(df.cost, bins, right=False, labels=labels) 日期转换：
import pandas;from pandas import read_csv;from pandas import to_datetime;df = read_csv("C:\\PA\\4.16\\data.csv",encoding="utf-8");df_dt=to_datetime(df.注册时间,format="%Y/%m/%d"); 日期格式化：
import pandas;from pandas import read_csv;from pandas import to_datetime;df = read_csv("C:\\PA\\4.16\\data.csv",encoding="utf-8");df_dt=to_datetime(df.注册时间,format="%Y/%m/%d");df_dt_str=df_dt.apply(lambda x:datatime.strftime(x,"%d-%m-%Y")) 日期抽取：
import pandas;from pandas import read_csv;from pandas import to_datetime;df = read_csv("C:\\PA\\4.18\\data.csv",encoding="utf-8");df_dt=to_datetime(df.注册时间,format="%Y/%m/%d");df_dt.dt.year;df_dt.dt.second;df_dt.dt.minute;df_dt.dt.hour;df_dt.dt.day;df_dt.dt.month;df_dt.dt.weekday;