This is the raw data
a=[[1,2,3,4,5,6],
[7,8,9,10,11,12]]
I want to convert it to the format like this:
b=[[1,2,3,7,8,9],
[4,5,6,10,11,12]]
array(List).swapaxes()
How to do it use reshape and swapaxes in python?
Thanks
try this:
import numpy as np
a = np.array([[1,2,3,4,5,6], [7,8,9,10,11,12]])
b = a.reshape((2, 2, 3)).swapaxes(0, 1).reshape(2, 6)
print(b)
check it out: https://coderpad.io/F7X7EKR4
Related
import numpy as np
import pandas as pd
df = pd.DataFrame({'dt': ['2021-2-13', '2022-2-15'],
'w': [5, 7],
'n': [11, 8]})
df.reset_index()
print(list(df.loc[:,'dt'].values))
gives: ['2021-2-13', '2022-2-15']
NEEDED: [('2021-2-13'), ('2022-2-15')]
Important (at comment's Q): "NEEDED" is the way "mplfinance" accepts vlines argument for plot (checked) - I need to draw vertical lines for specified dates at x-axis of chart
import mplfinance as mpf
RES['Date'] = RES['Date'].dt.strftime('%Y-%m-%d')
my_vlines=RES.loc[:,'Date'].values # NOT WORKS
fig, axlist = mpf.plot( ohlc_df, type="candle", vlines= my_vlines, xrotation=30, returnfig=True, figsize=(6,4))
will only work if explcit my_vlines= [('2022-01-18'), ('2022-02-25')]
SOLVED: Oh, it really appears to be so simple after all
my_vlines=list(RES.loc[:,'Date'].values)
Your question asks for a list of Numpy arrays but your desired output looks like Tuples. If you need Tuples, note that it's the comma that makes the tuple not the parentheses, so you'd do something like this:
desired_format = [(x,) for x in list(df.loc[:,'dt'].values)]
If you want numpy arrays, you could do this
desired_format = [np.array(x) for x in list(df.loc[:,'dt'].values)]
I think I understand your problem. Please see the example code below and let me know if this resolves your problem. I expanded on your dataframe to meet mplfinance plot criteria.
import pandas as pd
import numpy as np
import mplfinance as mpf
df = pd.DataFrame({'dt': ['2021-2-13', '2022-2-15'],'Open': [5,7],'Close': [11, 8],'High': [21,30],'Low': [7, 3]})
df['dt']=pd.to_datetime(df['dt'])
df.set_index('dt', inplace = True)
mpf.plot(df, vlines = dict(vlines = df.index.tolist()))
I want the output of this code in int64 format but the output of this code is in float. how can change it? pls suggest
import pandas as pd
import numpy as np
df = pd.read_csv('https://query.data.world/s/HqjNNadqEnwSq1qnoV_JqyRJkc7o6O')
df = df[df.isnull().sum(axis=1) < 5]
print(round(100*(df.isnull().sum()/len(df.index))),2)
Something like this should do the trick...
import pandas as pd
import numpy as np
df = pd.read_csv('https://query.data.world/s/HqjNNadqEnwSq1qnoV_JqyRJkc7o6O')
df = df[df.isnull().sum(axis=1) < 5]
x = round(100*(df.isnull().sum()/len(df.index)))
y = x.astype(np.int64)
print(y)
The key bit being x.astype(np.int64) to convert the format.
I am looking for a single vector with values [(0:400) (-400:-1)]
Can anyone help me on how to write this in python.
Using Numpy .array to create the vector and .arange to generate the range:
import numpy as np
arr = np.array([[np.arange(401)], [np.arange(-400, 0)]], dtype=object)
I am new to python and using numpy to read a csv into an array .So I used two methods:
Approach 1
train = np.asarray(np.genfromtxt(open("/Users/mac/train.csv","rb"),delimiter=","))
Approach 2
with open('/Users/mac/train.csv') as csvfile:
rows = csv.reader(csvfile)
for row in rows:
newrow = np.array(row).astype(np.int)
train.append(newrow)
I am not sure what is the difference between these two approaches? What is recommended to use?
I am not concerned which is faster since my data size is small but instead concerned more about differences in the resulting data type.
You can use pandas also, it is better and simple to use.
import pandas as pd
import numpy as np
dataset = pd.read_csv('file.csv')
# get all headers in csv
values = list(dataset.columns.values)
# get the labels, assuming last row is labels in csv
y = dataset[values[-1:]]
y = np.array(y, dtype='float32')
X = dataset[values[0:-1]]
X = np.array(X, dtype='float32')
So what is the difference in the result?
genfromtxt is the numpy csv reader. It returns an array. No need for an extra asarray.
The second expression is incomplete, looks like would produce a list of arrays, one for each line of the file. It uses the generic python csv reader which doesn't do much other than read a line and split it into strings.
New coder here, trying to run some t-tests in Python 3.6. Right now, to run my t-tests between my 2 data sets, I have been doing the following:
import plotly.plotly as py
import plotly.graph_objs as go
from plotly.tools import FigureFactory as FF
import numpy as np
import pandas as pd
import scipy
from scipy import stats
long_term_survivor_GENE1 = [-0.38,-0.99,-1.04,0.1, etc..]
short_term_survivor_GENE1 = [0.32, 0.33,0.96, etc...]
stats.ttest_ind(long_term_survivor_GENE1,short_term_survivor_GENE1)
Which requires me to manually enter the values for each column of both data sets for each specific gene (GENE1 in this case). Is there any way to be able to call for the values from the data set so that Python can just read the values without me typing them out myself? For example, some way that I can just say:
long_term_survivor_GENE1 = ##call values from GENE1 column from dataset 1##
short_term_survivor_GENE1 = ## call values from GENE1 column from dataset 2##
Thanks for any help, and sorry that I'm not very well-versed in this stuff. Appreciate any feedback/tips. If you have any other questions, please let me know!
If you've shoved your data into the columns of a pandas dataframe then it might be as easy as this.
>>> import pandas as pd
>>> long_term_survivor_GENE1 = [-0.38,-0.99,-1.04,0.1]
>>> short_term_survivor_GENE1 = [0.32, 0.33,0.96, 0.56]
>>> df = pd.DataFrame({'long_term_survivor_GENE1': long_term_survivor_GENE1, 'short_term_survivor_GENE1': short_term_survivor_GENE1})
>>> from scipy import stats
>>> stats.ttest_ind(df['long_term_survivor_GENE1'], df['short_term_survivor_GENE1'])
Ttest_indResult(statistic=-3.615804684179662, pvalue=0.011153077626049458)
It might be a good idea to review the statistics behind this though. If you haven't already got them in a dataframe then have a look for some of the many answers here on SO about using read_csv for assistance.