python for data analysis 2nd edition

Date-time data types and tools from Import = datetime.now ()print(now.year,now.month,now.day)# stores time in milliseconds Delta = now-datetime (1992,2,2)print(delta)print(delta.days) # You can subtraction from import timedeltaprinton the timestamp ( Delta+timedelta (12))String and DateTime conversions#Time Turn strTime_str = Now.strftime ('%y-%m-%d')Print(TIME_STR)#Str Turn TimeTIME_STR2 = Datetime.strptime (Time_str,'%y-%m-%d')Print(TIME_STR2)#wo

NumPy:NdarrayPandas:DataFrameMatplotlib:SciPy:IntegrateLinalgOptimizeSignalSparseSpecialStatsWeaveCanopy InstallationPython variable typeNumbers (digital)--int,long,float,complexString (String)-counting starting at 0, starting bit, ending (excluding end)List (lists)--can be changed, assignedTuple (tuple)--Non-changing assignmentDictionary (dictionary)--can be changed, assigned valuePython Data type conversionsPython operator--arithmetic operator, comp

Size of rental room (area)　　　　　　　　　 Rental Rate (price) Compare informationCode1 ImportMatplotlib as Mpl2 ImportMatplotlib.pyplot as Plt3 ImportPandas as Pad4 ImportSeaborn as SNS5 ImportNumPy as NP6 7Sns.set_style ('Dark')8KF = Pad.read_csv ('Kf.csv')9 Ten defSinplotone (): OneFig,ax =plt.subplots () AAx.violinplot (kf[' Price']) - plt.show () - the defsinplottwo (): -Sns.set_style ('Whitegrid') -Sns.boxplot (kf[' Price'],palette=' Deep') - #sns.despine (left=true) + plt.show (

U.S. scientists announced 11th that they first detected gravitational waves last September. This discovery confirms the prophecy of the physicist Einstein 100 years ago. Announcing the discovery was the head of the laser-interferometric gravitational Wave Observatory (LIGO). The institution was born in the 90 's and has been observed for nearly 30 years by gravitational wave observations. So the amount of gravitational wave data that is observed shou

automatically added as index Here you can simply replace index, generate a new series, People think, for NumPy, not explicitly specify index, but also can be through the shape of the index to the data, where the index is essentially the same as the numpy of the Shaping indexSo for the numpy operation, the same applies to pandas At the same time, it said that series is actually a dictionary, so you can also use a

Getting started with Python for data analysis--pandas Based on the NumPy established from pandas importSeries,DataFrame,import pandas as pd One or two kinds of data structure 1. Series A python-like dictionary with indexes and values

The way of thinking is mainly based on this article, linking:Http://www.jb51.net/article/78667.htmBut when the installation of brew, it may be the problem of the site, has been an errorSo looking for information from the Internet, brew installation needs a new window, non-root developmentSpecific methods refer to connection: http://jingyan.baidu.com/article/335530da8b2b0419cb41c338.htmlThe command line is as follows: Ruby-e "$ (curl-fssl https://raw.githubusercontent.com/Homebrew/install/m

Vectorization refers to using an array expression instead of a loop to manipulate each element in the array.The general functions provided by NumPy (both Ufunc functions) are functions that perform element-level operations on data in Ndarray. For example, the square function computes the square of each element, and the rint function rounds each element:There are also some functions that accept 2 parameters, called two ufunc, such as the Add function a

, the normal function can generate a sample array of 4*4: Samples = np.random.normal (size = (bis)) samplesout[]: Array ([[-1.22102285, 2.08688133, 1.15874399, 0.14342708], [-0.29772372, 0.36137871, 0.60243437, -0.09287792], [-0.49263459, 0.69445334, 1.02035894, -1.18263174], [-0.07184985,- 1.11834445, 0.89547984, 0.0585053]]) 3. ExampleRandom Walk 1000:nsteps = np.random.randint (0,2,size= Np.where (draws>0,1,-1= steps.cum

This blog is used to record learning and facilitate review review.PyplotIn the Matplotlib object-oriented drawing library, Pyplot is a convenient interface.Basic drawing functionsMp.plot (array of horizontal coordinates, array of vertical coordinates) 1 from __future__ import unicode_literals 2 import NumPy as NP 3 import Matplotlib.pyplot as MP 4 x = Np.linspace (-np.pi, Np.pi, 1000) 5 cos_y = Np.cos (x)/26 sin_y = Np.sin (x) 7 mp.plot (x, cos_y) 8 mp.plot (x, sin_y) 9 m

# mean averaging # std standard deviation # var asks for variance # min to find minimum # Max to find maximum value # argmin Minimum index # argmax Max indexXi. NumPy: Random number generationRandom number generation function within the Np.random sub-packageCommon functions: # Rand Given shape produces a random array (number between 0 and 1)# randint a given shape produces a random integer # Choice The given shape produces a random selection # Shuffle

#-*-coding:utf-8-*-"""Created on Thu Sep 12:24:37 2017@author:douzi"""ImportPandas as PD#User InformationUnames = ['user_id','Gender',' Age','Occupation','Zip']users= Pd.read_table ('Ch02/movielens/users.dat', sep='::', Header=none, Names=unames, engine='python')#Movie RankingsRnames = ['user_id','movie_id','rating','timestamp']ratings= Pd.read_table ('Ch02/movielens/ratings.dat', sep='::', Header=none, names=rnames,engine='

result object, together with the original object's index Df.groupby (' Smoker ', group_keys=false). Apply (Mean) A column that turns the grouped index into DF In some cases, the GroupBy as_index=false parameters are not used, and the resulting is a series, this situation is generally in spite of grouping, but the calculation needs to involve several columns, and finally get the Series,series index is a hierarchical index. This turns the series into a dataframe,series hierarchical index into a

How to: Enter in terminal Copy Code code as follows: Python weather.py http://www.weather.com.cn/weather/101010100.shtml Weather data in Beijing 6 days JSON format Copy Code code as follows: #coding =utf-8 #weather. py Import Urllib Import re Import Simplejson Import Sys If Len (SYS.ARGV)!= 2: print ' please enter:python ' + sys.argv[0] + ' Exit (0) url = sys.argv[1

First, Ipython basic functions1. Ipython is an interactive python command line2. Installation and use1 # Installation: Pip install Ipython 2 # use: Ipython is consistent with Python interpreter usageNote: Readers who want to learn machine learning are strongly advised to install Anaconda (including NumPy, pandas, etc.)Second, Ipython advanced features1. Basic use# -TAB key Auto-complete # -?: Introspection,

This article records some of the knowledge that appears in the book, convenient to use when the query. Implied volatility rate The implied volatility is the value of those fluctuations in the price of different options and the market quotations measured on the maturity date under other conditions unchanged.In this case, the implied volatility is not the input parameter of the model/formula, but the result of a digital optimization process of the Formula 4.1 basic

1.1. Foreword Here we use the Python m/r framework mrjob to analyze.1.2. M/R Steps Mapper: The form of parsing the row data into Key=hh value=1Shuffle: The result of passing the Shuffle will generate a value iterator sorted with key valuesResults such as: 09 [1, 1, 1 ... 1, 1]Reduce: We're here to figure out 09 hours of traffic.Output such as: sum ([1, 1, 1 ...) 1, 1])1.3. Code Cat mr_pv_hour.py#-*-Codin

] # - ifPattern.search (Invoice_number): # Use the RE module's search function to find patterns in the value of Invoice_number -Filewriter.writerow (row_list) # If the pattern appears in Invoice_number, write the line to the output file Pandas 1 #! /usr/bin/env Python32 3 ImportPandas as PD4 ImportSYS5 6Input_file = sys.argv[1]7Output_file = sys.argv[2]8 9Data_frame =pd.read_csv (input_file)TenData_frame_value_matches_pattern = data_frame.loc[data_frame['Invoice

different passenger levels and survivalThe more advanced the class, the greater the proportion of survival. The proportion of those who were not rescued in class 3 was significantly increased. Indicates whether the class is related to the existence of the accommodation.The relationship between 3.2.2 Sex and survivalIt can be found that most are concentrated in the 20-50-year-old, from the box-line chart to see the average age of nearly 30 years.Because age is a continuous value, we consider the

= ' right '). SUM ())When closing the right, The statistic is the 5 - minute cycle with 00:00:00 as the end, because the time is ahead to 1999-12-31 23:55:00 . 1999-12-31 23:55:00 02000-01-01 00:00:00 152000-01-01 00:05:00 402000-01-01 00:10:00 11So left or right closing depends on the start and end of the timeIn the financial world there is an omnipresent time-series aggregation, that is, the calculation of the 4 values of each polygon , the first value open: Open, the last value close: Close,