q.head()
Outputs
Weekly_Sales
Date
2010-02-28 131963.08
2010-03-31 91237.14
2010-04-30 150516.76
2010-05-31 66694.15
2010-06-30 66740.70
Now the problem i'm facing is that i want to plot 'Date' Column vs 'Weekly_Sales' Column. I've already used the command
q=y.resample('M',on='Date').sum()
to convert weekly data to monthly which results in the upper Dataframe.
type(q)
outputs "class 'pandas.core.frame.DataFrame'" showing that q is a data frame. Now since q doesn't have two different columns as shown here,
q.Weekly_Sales
outputs
Date
2010-02-28 131963.08
2010-03-31 91237.14
2010-04-30 150516.76
2010-05-31 66694.15
2010-06-30 66740.70
2010-07-31 81915.01
2010-08-31 64578.81
2010-09-30 71913.27
2010-10-31 134644.53
2010-11-30 92161.40
2010-12-31 173983.88
2011-01-31 69146.59
2011-02-28 125762.63
2011-03-31 82823.34
2011-04-30 165056.95
2011-05-31 68251.72
2011-06-30 62978.57
2011-07-31 78856.23
2011-08-31 59061.95
2011-09-30 87756.41
2011-10-31 98806.83
2011-11-30 98537.51
2011-12-31 174512.07
2012-01-31 70205.35
2012-02-29 134683.30
2012-03-31 114680.54
2012-04-30 125600.12
2012-05-31 70792.98
2012-06-30 83646.54
2012-07-31 66468.79
2012-08-31 83045.57
2012-09-30 76137.90
2012-10-31 96244.56
Freq: M, Name: Weekly_Sales, dtype: float64
whereas
q.Date
outputs
Traceback (most recent call last):
File "<pyshell#8>", line 1, in <module>
q.Date
File "C:\Program Files (x86)\Python36-32\lib\site-packages\pandas\core\generic.py", line 3614, in __getattr__
return object.__getattribute__(self, name)
AttributeError: 'DataFrame' object has no attribute 'Date'
since both the columns come under q.Weekly_Sales , how do i seperate them to get 2 columns and finally plot them?
double [[]] will query the single columns as dataframe rather than Series, then we using reset_index
new_s=q[['Weekly_Sales']].reset_index()
Related
from cgitb import text
from bs4 import BeautifulSoup
import requests
website = 'https://www.marketplacehomes.com/rent-a-home/'
result = requests.get(website)
content = result.text
soup = BeautifulSoup(content, 'html.parser')
lists = soup.find_all('div', class_=('tt-rental-row'))
for list in lists:
location = list.find('span', class_="renta;-adress")
beds = list.find('span', class_="renta;-beds")
baths = list.find('span', class_="renta;-beds")
availability = list.find('span', class_="rental-date-available")
info = [location, beds, baths, availability]
print(info)
If I try to run the last line of code, I get:
"IndentationError: expected an indented block"
If I try to run each indentation separately I get:
">>> location = list.find('span', class_="renta;-adress")
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
AttributeError: type object 'list' has no attribute 'find'"
I'm new to Python and I'm kinda stuck, can anyone please help me?
Note: Your code never runs the for-loop cause your selection never matches the elements in HTML. They are generated dynamically based on data from another ressource and requests do not render websites like a browser, it only uses static contents from response.
Be aware not to use built-in keywords they will cause errors, especialy in your case list.find() will raise one cause the type object 'list' do not has an attribute called find. You could simply check these things using type()
type(soup)
-> its a bs4.BeautifulSoup
type(soup.find_all('div', class_=('tt-rental-row')))
-> its a bs4.element.ResultSet
type(list)
-> its a type
So how to get your goal?
You could also use pandas to directly create a DataFrame and slice it to your needs:
import pandas as pd
pd.read_json('https://app.tenantturner.com/listings-json/2679')
Output:
id dateActivated latitude longitude address city state zip photo title ... baths dateAvailable rentAmount acceptPets applyUrl btnUrl btnText virtualTour propertyType enableWaitlist
0 83600 8/22/2022 35.750499 -86.393972 4481 Jack Faulk St Murfreesboro TN 37127 https://ttimages.blob.core.windows.net/propert... 4481 Jack Faulk St ... 2.0 Now 2195 cats, small dogs, large dogs https://app.propertyware.com/pw/application/#/... https://app.tenantturner.com/qualify/4481-jack... Schedule Viewing None Single Family False
1 100422 8/31/2022 30.277607 -95.472842 213 Skybranch Court Conroe TX 77304 https://ttimages.blob.core.windows.net/propert... 213 Skybranch Court ... 2.5 Now 2100 cats, small dogs, large dogs https://app.propertyware.com/pw/application/#/... https://app.tenantturner.com/qualify/213-skybr... Schedule Viewing None Condo Unit False
2 106976 7/27/2022 28.274720 -82.298077 8127 Olive Brook Dr Wesley Chapel FL 33545 https://ttimages.blob.core.windows.net/propert... 8127 Olive Brook Dr ... 2.0 Now 2650 no pets https://app.propertyware.com/pw/application/#/... https://app.tenantturner.com/qualify/8127-oliv... Schedule Viewing None Single Family False
3 116188 8/15/2022 42.624023 -83.144614 735 Grace Ave Rochester Hills MI 48307 https://ttimages.blob.core.windows.net/propert... 735 Grace Ave ... 2.0 Now 1600 cats, small dogs, large dogs https://app.propertyware.com/pw/application/#/... https://app.tenantturner.com/qualify/735-grace... Schedule Viewing None Single Family False
4 126846 8/22/2022 32.046455 -81.071181 1810 E 41st St Savannah GA 31404 https://ttimages.blob.core.windows.net/propert... 1810 E 41st St ... 1.0 Now 1395 small dogs https://app.propertyware.com/pw/application/#/... https://app.tenantturner.com/qualify/1810-e-41... Schedule Viewing None Single Family True
...
91 rows × 22 columns
Example:
To show only specifc columns, simply pass a list of there names.
import pandas as pd
pd.read_json('https://app.tenantturner.com/listings-json/2679')[['address', 'city','state', 'zip', 'title', 'beds', 'baths','dateAvailable']]
Output
address beds baths dateAvailable
0 4481 Jack Faulk St 4 2.0 Now
1 213 Skybranch Court 3 2.5 Now
2 8127 Olive Brook Dr 3 2.0 Now
3 735 Grace Ave 3 2.0 Now
4 1810 E 41st St 3 1.0 Now
... ... ... ... ...
91 rows × 4 columns
Since the word list is a built-in keyword in python you can't use it as variable name try another name
for myList in lists:
location = myList.find('span', class_="renta;-adress")
beds = myList.find('span', class_="renta;-beds")
baths = myList.find('span', class_="renta;-beds")
availability = myList.find('span', class_="rental-date-available")
info = [location, beds, baths, availability]
print(info)
I am trying to do test to see if a data type is 'datetime.time' and if so convert it to 'datetime.datetime'. My code snippet is below. x_values is a series and each element of the series is a 'datetime.time'.
...
x_values = x.loc[:, "processed_time"]
print(x_values.dtypes)
print(type(x_values.iloc[0]))
print(x_values)
if isinstance(x_values.iloc[0], datetime.time):
x_values = pd.to_datetime(x_values, format='%H:%M:%S')
...
But the program errors out at the test with:
Traceback (most recent call last):
File "/Users/.../risk_calculations.py", line 282, in plot_risk
if isinstance(x_values.iloc[0], datetime.time):
TypeError: isinstance() arg 2 must be a type or tuple of types
object
<class 'datetime.time'>
1387 00:55:14
1388 10:02:01
1389 10:02:02
1390 10:02:02
1391 10:02:08
...
6417 14:36:49
6418 14:36:51
6419 15:24:52
6420 15:36:59
6422 16:21:03
Name: processed_time, Length: 3621, dtype: object
This Stack answer seemed closest to addressing my challenge but I think I have implemented the suggestion correctly. Note that the print statements show that the type is in fact a 'class datetime.time' as required (I think) by 'is instance' so I don't understand why the errors. I know I can make it work if I replace the 'if' statement with:
if 'datetime.time' in str(type(x_values.iloc[0])):
...
But that seems kludgy. Is there a more correct test for an instance of 'datetime.time'?
I am trying to iterate this list with words as
CTCCTC TCCTCT CCTCTC CTCTCC TCTCCC CTCCCA TCCCAA CCCAAA CCAAAC CAAACT
CTGGGC TGGGCC GGGCCA GGCCAA GCCAAT CCAATG CAATGC AATGCC ATGCCT TGCCTG GCCTGC
TGCCAG GCCAGG CCAGGA CAGGAG AGGAGG GGAGGG GAGGGG AGGGGC GGGGCT GGGCTG GGCTGG GCTGGT CTGGTC
TGGTCT GGTCTG GTCTGG TCTGGA CTGGAC TGGACA GGACAC GACACT ACACTA CACTAT
ATTCAG TTCAGC TCAGCC CAGCCA AGCCAG GCCAGT CCAGTC CAGTCA AGTCAA GTCAAC TCAACA CAACAC AACACA
ACACAA CACAAG ACAAGG AGGTGG GGTGGC GTGGCC TGGCCT GGCCTG GCCTGC CCTGCA CTGCAC
TGCACT GCACTC CACTCG ACTCGA CTCGAG TCGAGG CGAGGT GAGGTT AGGTTC GGTTCC
TATATA ATATAC TATACC ATACCT TACCTG ACCTGG CCTGGT CTGGTA TGGTAA GGTAAT GTAATG TAATGG AATGGA
I am trying for loop to read each item in the list and parse it through mk_model.vector
the code used is as follows
for x in all_seq_sentences[:]:
mk_model.vector(x)
print(x)
Usually, mk_model.vector("AGT") will give an array corresponding to defines dna2vec model, But here rather than actually performing the model run it throws error as
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
<ipython-input-144-77c47b13e98a> in <module>
1 for x in all_seq_sentences[:]:
----> 2 mk_model.vector(x)
3 print(x)
4
~/Desktop/DNA2vec/dna2vec/dna2vec/multi_k_model.py in vector(self, vocab)
35
36 def vector(self, vocab):
---> 37 return self.data[len(vocab)].model[vocab]
38
39 def unitvec(self, vec):
KeyError: 664
Looking forward to some help here
The above problem was having issues because the for loop took all items in first line as one item, which is why .split() was best solution of it. To read follow https://python-reference.readthedocs.io/en/latest/docs/str/split.html
working code:
for i in all_seq_sentences:
word = i.split()
print(word[0])
and then later implement another loop to access the model.vector function
vec_of_all_seq = []
for sentence in all_seq_sentences:
sentence = sentence.split()
for word in sentence:
vec_of_all_seq.append(mk_model.vector(word))
vector representation derived from model.vector will be saved in numpy array named vec_of_all_seq.
I am trying to use SEC (U.S. Security and Exchange Commision data). The SEC provides useful data in a txtformat. I am using
Financial Statement Data Sets for the second quarter of 2017. You can find the data I use here.
I try to read the txtfiles into a pandas dataframe. I tried it the following ways:
sub = pd.read_fwf('sub.txt')
sub_1 = pd.read_csv('sub.txt')
I get no error with using Pandas' read_fwf function - but the output is utter rubbish. Here is the head of the dataframe:
adsh cik name sic countryba stprba cityba zipba bas1 bas2 baph countryma stprma cityma zipma mas1 mas2 countryinc stprinc ein former changed afs wksi fye form period fy fp filed accepted prevrpt detail instance nciks aciks Unnamed: 1
0 0000002178-17-000038\t2178\tADAMS RESOURCES & ... NaN
1 0000002488-17-000107\t2488\tADVANCED MICRO DEV... NaN
I do get an error when using read_csv: Error tokenizing data. C error: Expected 2 fields in line 7, saw 3
Any ideas on how tor read the data into a pandas dataframe?
It looks like the files are tab separated - that's why you're seeing \t in the results. pandas read_csv defaults to comma separated values, so you have to change the separator. This is controlled by the sep parameter. In addition, you will need to provide the proper encoding (errors are thrown when trying to read the num, pre, and tag files). Generally ISO-8859-1 is a good choice.
#import pandas
import pandas as pd
#read in the .txt file and choose a separator and encoding standard
df = pd.read_csv('sub.txt', sep='\t', encoding='ISO-8859-1')
#output the results
print(df)
adsh cik name \
0 0000002178-17-000038 2178 ADAMS RESOURCES & ENERGY, INC.
1 0000002488-17-000107 2488 ADVANCED MICRO DEVICES INC
2 0000002969-17-000019 2969 AIR PRODUCTS & CHEMICALS INC /DE/
3 0000002969-17-000024 2969 AIR PRODUCTS & CHEMICALS INC /DE/
4 0000003499-17-000010 3499 ALEXANDERS INC
5 0000003545-17-000043 3545 ALICO INC
6 0000003570-17-000073 3570 CHENIERE ENERGY INC
I am currently building a binary classification model and have created an input file for svm-train (svm_input.txt). This input file has 453 lines, 4 No. features and 2 No. classes [0,1].
i.e
0 1:15.0 2:40.0 3:30.0 4:15.0
1 1:22.73 2:40.91 3:36.36 4:0.0
1 1:31.82 2:27.27 3:22.73 4:18.18
0 1:22.73 2:13.64 3:36.36 4:27.27
1 1:30.43 2:39.13 3:13.04 4:17.39 ......................
My problem is that when I count the number of lines in the output model generated by svm-train (svm_train_model.txt), this has 12 fewer lines than that of the input file. The line count here shows 450, although there are obviously also 9 lines at the beginning showing the various parameters generated
i.e.
svm_type c_svc
kernel_type rbf
gamma 1
nr_class 2
total_sv 441
rho -0.156449
label 0 1
nr_sv 228 213
SV
Therefore 12 lines in total from the original input of 453 have gone. I am new to svm and was hoping that someone could shed some light on why this might have happened?
Thanks in advance
Updated.........
I now believe that in generating the model, it has removed lines whereby the labels and all the parameters are exactly the same.
To explain............... My input is a set of miRNAs which have been classified as 1 and 0 depending on their involvement in a particular process or not (i.e 1=Yes & 0=No). The input file looks something like.......
0 1:22 2:30 3:14 4:16
1 1:26 2:15 3:17 4:25
0 1:22 2:30 3:14 4:16
Whereby, lines one and three are exactly the same and as a result will be removed from the output model. My question is then both why the output model would do this and how I can get around this (whilst using the same features)?
Whilst both SOME OF the labels and their corresponding feature values are identical within the input file, these are still different miRNAs.
NOTE: The Input file does not have a feature for miRNA name (and this would clearly show the differences in each line) however, in terms of the features used (i.e Nucleotide Percentage Content), some of the miRNAs do have exactly the same percentage content of A,U,G & C and as a result are viewed as duplicates and then removed from the output model as it obviously views them as duplicates even though they are not (hence there are less lines in the output model).
the format of the input file is:
Where:
Column 0 - label (i.e 1 or 0): 1=Yes & 0=No
Column 1 - Feature 1 = Percentage Content "A"
Column 2 - Feature 2 = Percentage Content "U"
Column 3 - Feature 3 = Percentage Content "G"
Column 4 - Feature 4 = Percentage Content "C"
The input file actually looks something like (See the very first two lines below), as they appear identical, however each line represents a different miRNA):
1 1:23 2:36 3:23 4:18
1 1:23 2:36 3:23 4:18
0 1:36 2:32 3:5 4:27
1 1:14 2:41 3:36 4:9
1 1:18 2:50 3:18 4:14
0 1:36 2:23 3:23 4:18
0 1:15 2:40 3:30 4:15
In terms of software, I am using libsvm-3.22 and python 2.7.5
Align your input file properly, is my first observation. The code for libsvm doesnt look for exactly 4 features. I identifies by the string literals you have provided separating the features from the labels. I suggest manually converting your input file to create the desired input argument.
Try the following code in python to run
Requirements - h5py, if your input is from matlab. (.mat file)
pip install h5py
import h5py
f = h5py.File('traininglabel.mat', 'r')# give label.mat file for training
variables = f.items()
labels = []
c = []
import numpy as np
for var in variables:
data = var[1]
lables = (data.value[0])
trainlabels= []
for i in lables:
trainlabels.append(str(i))
finaltrain = []
trainlabels = np.array(trainlabels)
for i in range(0,len(trainlabels)):
if trainlabels[i] == '0.0':
trainlabels[i] = '0'
if trainlabels[i] == '1.0':
trainlabels[i] = '1'
print trainlabels[i]
f = h5py.File('training_features.mat', 'r') #give features here
variables = f.items()
lables = []
file = open('traindata.txt', 'w+')
for var in variables:
data = var[1]
lables = data.value
for i in range(0,1000): #no of training samples in file features.mat
file.write(str(trainlabels[i]))
file.write(' ')
for j in range(0,49):
file.write(str(lables[j][i]))
file.write(' ')
file.write('\n')