data-analysis

问题 I have a set of data. It is obviously have some periodic nature. I want to find out what frequency it has by using the fourier transformation and plot it out. Here is a shot of mine, but it seems not so good. This is the corresponding code, I don't konw why it fails: import numpy from pylab import * from scipy.fftpack import fft,fftfreq import matplotlib.pyplot as plt dataset = numpy.genfromtxt(fname='data.txt',skip_header=1) t = dataset[:,0] signal = dataset[:,1] npts=len(t) FFT = abs(fft

问题 This question already has answers here : Pandas Merging 101 (2 answers) Closed last year . I have two data frames, df1=pd.DataFrame({"Req":["Req 1","Req 2","Req 3"],"Count":[1,2,1]}) Req Count 0 Req 1 1 1 Req 2 2 2 Req 3 1 df2=pd.DataFrame({"Req":["Req 1","Req 2"],"Count":[0,1]}) Req Count 0 Req 1 0 1 Req 2 1 I am trying to merge these df's based on "Req" column My desired output is, Req total from_1 from_2 Req 1 1 1 0 Req 2 3 2 1 Req 3 1 1 0 I tried pd.merge(df1, df2, on = "Req", ) but it is

问题 I have a test dataset and train dataset as below. I have provided a sample data with min records, but my data has than 1000's of records. Here E is my target variable which I need to predict using an algorithm. It has only four categories like 1,2,3,4. It can take only any of these values. Training Dataset: A B C D E 1 20 30 1 1 2 22 12 33 2 3 45 65 77 3 12 43 55 65 4 11 25 30 1 1 22 23 19 31 2 31 41 11 70 3 1 48 23 60 4 Test Dataset: A B C D E 11 21 12 11 1 2 3 4 5 6 7 8 99 87 65 34 11 21 24

问题 I have a test dataset and train dataset as below. I have provided a sample data with min records, but my data has than 1000's of records. Here E is my target variable which I need to predict using an algorithm. It has only four categories like 1,2,3,4. It can take only any of these values. Training Dataset: A B C D E 1 20 30 1 1 2 22 12 33 2 3 45 65 77 3 12 43 55 65 4 11 25 30 1 1 22 23 19 31 2 31 41 11 70 3 1 48 23 60 4 Test Dataset: A B C D E 11 21 12 11 1 2 3 4 5 6 7 8 99 87 65 34 11 21 24

问题 One of the things I deal with most in data cleaning is missing values. R deals with this well using its "NA" missing data label. In python, it appears that I'll have to deal with masked arrays which seem to be a major pain to set up and don't seem to be well documented. Any suggestions on making this process easier in Python? This is becoming a deal-breaker in moving into Python for data analysis. Thanks Update It's obviously been a while since I've looked at the methods in the numpy.ma

问题 One of the things I deal with most in data cleaning is missing values. R deals with this well using its "NA" missing data label. In python, it appears that I'll have to deal with masked arrays which seem to be a major pain to set up and don't seem to be well documented. Any suggestions on making this process easier in Python? This is becoming a deal-breaker in moving into Python for data analysis. Thanks Update It's obviously been a while since I've looked at the methods in the numpy.ma

问题 I have specific case where I want to convert this df: print df Schoolname Attribute Value 0 xyz School Safe 3.44 1 xyz School Cleanliness 2.34 2 xyz School Money 4.65 3 abc School Safe 4.40 4 abc School Cleanliness 4.50 5 abc School Money 4.90 6 lmn School Safe 2.34 7 lmn School Cleanliness 3.89 8 lmn School Money 4.65 Which i need to get in this format so that i can convert it to numpy array for linear regression modelling. required_df: Schoolname Safe Cleanliness Money 0 xyz School 3.44 2

问题 So, this is my data frame. PatientNumber QT Answer Answerdate DiagnosisDate 1 1 transferring No 2017-03-03 2018-05-03 2 1 preparing food No 2017-03-03 2018-05-03 3 1 medications Yes 2017-03-03 2018-05-03 4 2 transferring No 2011-05-10 2012-05-04 5 2 preparing food No 2011-05-10 2012-05-04 6 2 medications No 2011-05-10 2012-05-04 7 2 transferring Yes 2011-15-03 2012-05-04 8 2 preparing food Yes 2011-15-03 2012-05-04 9 2 medications No 2011-15-03 2012-05-04 10 2 transferring Yes 2010-15-12 2012

问题 Setup I have a DataFrame with three columns: "Category" contains True and False, and I have done df.groupby('Category') to group by these values. "Time" contains timestamps (measured in seconds) at which values have been recorded "Value" contains the values themselves. At each time instance, two values are recorded: one has category "True", and the other has category "False". Rolling apply question Within each category group , I want to compute a number and store it in column Result for each

问题 I have five columns[each column name represents each candidate say.. can1 can2 can3 can4 can5 , each column has binary data(TRUE OR FALSE) and I have another column-CANDIDATES which has the data collection with names of the 5 candidates(factor=5)(the same candidates). so it is something like can1 can2 can3 can4 can5 CANDIDATES I want to create column which is binary, in which the row is true if the element of the CANDIDATE and the corresponding candidate column(in the 5 column) is true..