I have an ongoing project with the following assignment: Given data in csv files for ocean flows in two sets of vectors over a lapse of time. Therefore I have, let’s say U vectors for latitudinal and V vectors for longitudinal areas over time. I’m working in Python using Jupyter Notebook and numpy. My U ..

#### Category : correlation

What if I want to analyze a time series, but the period is not constant? for example : 1th period : 34 s 2th period : 33 s 3th period : 35 s 4th period : 34 s In this situation, how can we prove that there is a cycle for each period? Source: Python ..

When I use pandas.DataFrame.corr() to create a correlation matrix, I found the correlation matrix has 38 columns and the DataFrame has 81 columns. In my mind, these two columns should be the same. So why the two columns are not equal? The code corr_matrix = train.corr(method="kendall").abs() print("original DataFrame Shape {}".format(train.shape)) print("correlation matrix shape {}".format(corr_matrix.shape)) The ..

Hi, If a correlation diagram is like this, how to interpret it? Surprisingly, the Person Correlation is more than 0.7, so wondering if it is the right understanding? Z Source: Python..

For my current project I am using sklearn.cross_decomposition.CCA. On several wepages (e.g. https://stats.idre.ucla.edu/r/dae/canonical-correlation-analysis/ or https://www.uaq.mx/statsoft/stcanan.html) it says that canonical loadings can be computed as correlations between variables and the canonical variates. However, I could not reproduce this using scipy.stats.pearsonr? Is this just false information or am I doing something wrong? Here’s an example from sklearn.cross_decomposition ..

I am working on house price prediction data on Kaggle; enter link description here. In order to do feature selection, I thought of factorizing categorical variables as numbers and find if possible issues of multicollinearity. For example, there are two categorical vars I used; BHK_OR_RK: values are 0 or 1. READY_TO_MOVE: values are 0 and ..

I have a dataframe like this (the real DF has 94 columns and 40 rows): NAME TIAS EFGA SOE KERA CODE SURVIVAL SOAP corp 1.391164e+10 1.265005e+10 0.000000e+00 186522000.0 366 21 NiANO inc 42673.0 0.0 0.0 42673.0 366 3 FFS jv 9.523450e+05 NaN NaN 8.754379e+09 737 4 KELL Corp 1.045967e+07 9.935970e+05 0.000000e+00 NaN 737 4 Os ..

I’ve got an entire front-end running which takes input from a user to draw a curve and this curve now needs to be cross-verified with what the correct answer should be which I already have information about. I extracted information about the Y-coordinates for every corresponding X-coordinate in both the user input and the correct ..

I have some problem with the following code. I am trying to change the scale of X axis and Y axis to Log using axs.set_yscale(‘log’) and axs.set_xscale(‘log’). But the fitted line to the data is curved if I do that. Any idea how to solve it? Here is the code along with output Source: Python ..

I have a dataframe with an ‘EVENT’ column that has three variables called click, pageview and preview. I want to check for any correlation between clicks and previews on a link? If there is, is it significant and how large is the effect? And I want to test for potential linear and binary relationships between ..

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