# -*- coding: utf-8 -*-

import numpy as np

import pandas as pd

import sys

from pandas import Series, DataFrame

###pandas

#Series

obj = Series([4, 7, -5, 3])

obj

obj.values

obj.index

obj2 = Series([4, 7, -5, 3], index=['d', 'b', 'a', 'c'])

obj2

obj2.index

obj2['a']

obj2['d'] = 6

obj2[['c', 'a', 'd']]

obj2[obj2 > 0]

obj2 * 2

np.exp(obj2)

'b' in obj2

'e' in obj2

sdata = {'Ohio': 35000, 'Texas': 71000, 'Oregon': 16000, 'Utah': 5000}

obj3 = Series(sdata)

obj3

states = ['California', 'Ohio', 'Oregon', 'Texas']

obj4 = Series(sdata, index=states)

obj4

pd.isnull(obj4)

pd.notnull(obj4)

obj4.isnull()

obj3

obj4

obj3 + obj4

obj4.name = 'population'

obj4.index.name = 'state'

obj4

obj.index = ['Bob', 'Steve', 'Jeff', 'Ryan']

obj

#dataframe

data = {'state': ['Ohio', 'Ohio', 'Ohio', 'Nevada', 'Nevada'],

'year': [2000, 2001, 2002, 2001, 2002],

'pop': [1.5, 1.7, 3.6, 2.4, 2.9]}

frame = DataFrame(data)

frame

DataFrame(data, columns=['year', 'state', 'pop'])

frame2 = DataFrame(data, columns=['year', 'state', 'pop', 'debt'],

index=['one', 'two', 'three', 'four', 'five'])

frame2

frame2.columns

frame2['state']

frame2.year

frame2.ix['three']

frame2['debt'] = 16.5

frame2

frame2['debt'] = np.arange(5.)

frame2

val = Series([-1.2, -1.5, -1.7], index=['two', 'four', 'five'])

frame2['debt'] = val

frame2

frame2['eastern'] = frame2.state == 'Ohio'

frame2

del frame2['eastern']

frame2.columns

pop = {'Nevada': {2001: 2.4, 2002: 2.9},

'Ohio': {2000: 1.5, 2001: 1.7, 2002: 3.6}}

frame3 = DataFrame(pop)

frame3

frame3.T

DataFrame(pop, index=[2001, 2002, 2003])

pdata = {'Ohio': frame3['Ohio'][:-1],

'Nevada': frame3['Nevada'][:2]}

DataFrame(pdata)

frame3.index.name = 'year'; frame3.columns.name = 'state'

frame3

frame3.values

frame2.values

#索引对象

obj = Series(range(3), index=['a', 'b', 'c'])

index = obj.index

index

index[1:]

index[1] = 'd'

index = pd.Index(np.arange(3))

obj2 = Series([1.5, -2.5, 0], index=index)

obj2.index is index

frame3

'Ohio' in frame3.columns

2003 in frame3.index

###数据读取

#读取文本格式数据

df = pd.read_csv('d:data/ex1.csv')

df

pd.read_table('d:data/ex1.csv', sep=',')

pd.read_csv('d:data/ex2.csv', header=None)

pd.read_csv('d:data/ex2.csv', names=['a', 'b', 'c', 'd', 'message'])

names = ['a', 'b', 'c', 'd', 'message']

pd.read_csv('d:data/ex2.csv', names=names, index_col='message')

parsed = pd.read_csv('d:data/csv_mindex.csv', index_col=['key1', 'key2'])

parsed

list(open('d:data/ex3.txt'))

result = pd.read_table('d:data/ex3.txt', sep='\s+')

result

pd.read_csv('d:data/ex4.csv', skiprows=[0, 2, 3])

result = pd.read_csv('d:data/ex5.csv')

result

pd.isnull(result)

result = pd.read_csv('d:data/ex5.csv', na_values=['NULL'])

result

sentinels = {'message': ['foo', 'NA'], 'something': ['two']}

pd.read_csv('d:data/ex5.csv', na_values=sentinels)

#逐行读取文本文件

result = pd.read_csv('d:data/ex6.csv')

result

pd.read_csv('d:data/ex6.csv', nrows=5)

chunker = pd.read_csv('d:data/ex6.csv', chunksize=1000)

chunker

chunker = pd.read_csv('d:data/ex6.csv', chunksize=1000)

tot = Series([])

for piece in chunker:

tot = tot.add(piece['key'].value_counts(), fill_value=0)

tot = tot.order(ascending=False)

tot[:10]

#文件写出

data = pd.read_csv('d:data/ex5.csv')

data

data.to_csv('d:data/out.csv')

data.to_csv(sys.stdout, sep='|')

data.to_csv(sys.stdout, na_rep='NULL')

data.to_csv(sys.stdout, index=False, header=False)

data.to_csv(sys.stdout, index=False, columns=['a', 'b', 'c'])

dates = pd.date_range('1/1/2000', periods=7)

ts = Series(np.arange(7), index=dates)

ts.to_csv('tseries.csv')

Series.from_csv('tseries.csv', parse_dates=True)

#手工处理分隔符格式

import csv

f = open('d:data/ex7.csv')

reader = csv.reader(f)

for line in reader:

print(line)

lines = list(csv.reader(open('d:data/ex7.csv')))

header, values = lines[0], lines[1:]

data_dict = {h: v for h, v in zip(header, zip(*values))}

data_dict

class my_dialect(csv.Dialect):

lineterminator = '\n'

delimiter = ';'

quotechar = '"'

quoting = csv.QUOTE_MINIMAL

with open('mydata.csv', 'w') as f:

writer = csv.writer(f, dialect=my_dialect)

writer.writerow(('one', 'two', 'three'))

writer.writerow(('1', '2', '3'))

writer.writerow(('4', '5', '6'))

writer.writerow(('7', '8', '9'))

pd.read_table('mydata.csv', sep=';')

#Excel数据

#生成xls工作薄

import xlrd, xlwt

path = 'd:data/'

wb = xlwt.Workbook()

wb

wb.add_sheet('first_sheet', cell_overwrite_ok=True)

wb.get_active_sheet()

ws_1 = wb.get_sheet(0)

ws_1

ws_2 = wb.add_sheet('second_sheet')

data = np.arange(1, 65).reshape((8, 8))

data

ws_1.write(0, 0, 100)

for c in range(data.shape[0]):

for r in range(data.shape[1]):

ws_1.write(r, c, data[c, r])

ws_2.write(r, c, data[r, c])

wb.save(path + 'workbook.xls')

#生成xlsx工作薄

#从工作薄中读取

book = xlrd.open_workbook(path + 'workbook.xls')

book

book.sheet_names()

sheet_1 = book.sheet_by_name('first_sheet')

sheet_2 = book.sheet_by_index(1)

sheet_1

sheet_2.name

sheet_1.ncols, sheet_1.nrows

cl = sheet_1.cell(0, 0)

cl.value

cl.ctype

sheet_2.row(3)

sheet_2.col(3)

sheet_1.col_values(3, start_rowx=3, end_rowx=7)

sheet_1.row_values(3, start_colx=3, end_colx=7)

for c in range(sheet_1.ncols):

for r in range(sheet_1.nrows):

print '%i' % sheet_1.cell(r, c).value,

print

#使用pandas读取

xls_file=pd.ExcelFile(path + 'workbook.xls')

table=xls_file.parse('first_sheet')

#JSON数据

obj = """

{"name": "Wes",

"places_lived": ["United States", "Spain", "Germany"],

"pet": null,

"siblings": [{"name": "Scott", "age": 25, "pet": "Zuko"},

{"name": "Katie", "age": 33, "pet": "Cisco"}]

}

"""

import json

result = json.loads(obj)

result

asjson = json.dumps(result)

siblings = DataFrame(result['siblings'], columns=['name', 'age'])

siblings

#二进制数据格式

#pickle

frame = pd.read_csv('d:data/ex1.csv')

frame

frame.to_pickle('d:data/frame_pickle')

pd.read_pickle('d:data/frame_pickle')

#HDF5格式

store = pd.HDFStore('mydata.h5')

store['obj1'] = frame

store['obj1_col'] = frame['a']

store

store['obj1']

store.close()

os.remove('mydata.h5')

#使用HTML和Web API

import requests

url = 'https://api.github.com/repos/pydata/pandas/milestones/28/labels'

resp = requests.get(url)

resp

data=json.loads(resp.text)

issue_labels = DataFrame(data)

issue_labels

#使用数据库

import sqlite3

query = """

CREATE TABLE test

(a VARCHAR(20), b VARCHAR(20),

c REAL,        d INTEGER

);"""

con = sqlite3.connect(':memory:')

con.execute(query)

con.commit()

data = [('Atlanta', 'Georgia', 1.25, 6),

('Tallahassee', 'Florida', 2.6, 3),

('Sacramento', 'California', 1.7, 5)]

stmt = "INSERT INTO test VALUES(?, ?, ?, ?)"

con.executemany(stmt, data)

con.commit()

cursor = con.execute('select * from test')

rows = cursor.fetchall()

rows

cursor.description

DataFrame(rows, columns=zip(*cursor.description)[0])

import pandas.io.sql as sql

sql.read_sql('select * from test', con)