Tag : Python

The popular initializers include RandomNormal distribution function, trancated normal distribution function, GlorotNormal function, HeNormal function and others. This example code show what are these functions and display the probability density functions of these initializers for my reference. Code: import numpy as np import matplotlib.pyplot as plt from scipy.stats import norm x = np.linspace(-10,10,500) #default RandomNormal(mean=0.0, ..

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Select a subset of xarray data for a specified year range with Python by using the sel() method. Here’s an example: import xarray as xr # Load your xarray dataset ds = xr.open_dataset(‘my_dataset.nc’) # Select a subset of data for a specified year range subset = ds.sel(time=slice(‘start_year’, ‘end_year’)) Replace my_dataset.nc with the name of your ..

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There are a few Python packages that can be used to read RData files. Here are three popular ones: #install rpy2 !pip install rpy2 #An example of how to use rpy2 to load an RData file: #—————————————————- import rpy2.robjects as robjects # Load RData file robjects.r[‘load’](‘file.RData’) # Access R objects from Python r_var = robjects.globalenv[‘var_name’] ..

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The following code is used to count the order of the date of February 29 in leap years for a period. import sys import warnings if not sys.warnoptions: warnings.simplefilter(‘ignore’) #Process data import numpy as np import matplotlib.pyplot as plt #Process data import numpy as np import xarray as xr #Writing data files import pandas as ..

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a beautiful landscape

Group DataFrame using a mapper or by a Series of columns.Return a GroupBy object, grouped by values in column named “col”.Grouping and aggregation functions to help you to learn features of your dataset, like the sum, mean, or average value of a group of elements. The sample code below may help. Prepare data import pandas as ..

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A beautiful extreme wide aerial view of snowy mountains

It is easy to change the layout, sort, reindex, rename, and subset table data using pandas commands. The simple code below shows you how to do this easily. The functions include melt(),pivot,sort_values,rename,sort_index,drop,filter,query,iloc,loc,iat,at and drop_duplicates. Prepare data #dowonload https://github.com/ziwangdeng/Data/blob/main/Vancouver_weather2010to2019_v00.csv import pandas as pd df=pd.read_csv(‘Vancouver_weather2010to2019_v00.csv’) cols=df.columns df1=df[cols[:10]] df2=df[cols[10:]] ll=len(df) df3=df.head(5000) df4=df.tail(ll-5000) df.columns Index([‘Unnamed: 0’, ‘Longitude (x)’, ‘Latitude ..

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Beautiful happy picturesque charming lone maple trees

A comprehensive understanding of the overall situation of the data is the first step in data analysis. The following sample code shows you how to do this simply with pandas. Use of functions columns, keys(),axes,dtypes,info(),describe(),describe(include=object),isna().sum(),nunique(),value_counts(),len,shape,nsmallest(), nlargest(),sample(),head and tail. Read data #dowonload https://github.com/ziwangdeng/Data/blob/main/Vancouver_weather2010to2019_v00.csv import pandas as pd df=pd.read_csv(‘Vancouver_weather2010to2019_v00.csv’) Check column names df.columns Index([‘Unnamed: 0’, ‘Longitude (x)’, ..

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Beautiful trees landscape image by AI

Example code for transforming a selected group of variables with Sklearn Transformer Wrapper. It can also answer following questions: Prepare data sample import numpy as np import pandas as pd import matplotlib.pyplot as plt from sklearn.model_selection import train_test_split from sklearn.impute import SimpleImputer from sklearn.preprocessing import StandardScaler from sklearn.preprocessing import OneHotEncoder from sklearn.preprocessing import PolynomialFeatures from ..

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