虽然使用循环并不太糟糕,但在处理大量的分箱时,这种方法可能会变得效率低下,因为需要将该过程重复N次(箱子数量)。获取分箱数据的一种更简单的方法是使用pandas的cut方法,具体参见:《Pandas基础:使用Cut方法进行数据分箱(Binning Data)》。 注:本文学习整理自pythoninoffice.com,供有兴趣的朋友参考。
1. 数据分箱 1.1 等区间分箱 将连续变量的值进行获取,然后利用pandas的cut函数进行等区间分箱。 如下代码,获取值A2_values ,并等数值区间分为6类为[0,1,2,3,4,5]; (cut在操作时,统计了一维数组的最小、最大值,得到一个区间长度,因为需要划分6个区间) 1.2 等频分箱 将连续变量在[min,max]区间内,等...
1. 数据分箱 1.1 等区间分箱 将连续变量的值进行获取,然后利用pandas的cut函数进行等区间分箱。 如下代码,获取值A2_values ,并等数值区间分为6类为[0,1,2,3,4,5]; (cut在操作时,统计了一维数组的最小、最大值,得到一个区间长度,因为需要划分6个区间) 1.2 等频分箱 将连续变量在[min,max]区间内,等...
# 创建环境conda虚拟环境 conda create -c bioconda -n vRhyme python=3 networkx pandas numpy numba scikit-learn pysam samtools mash mummer mmseqs2 prodigal bowtie2 bwa # 激活conda虚拟环境 conda activate vRhyme # 从Github下载安装包,并使用pip安装 git clone https://github.com/AnantharamanLab/vRhyme...
xvalues列是这里最棘手的一个,因为我必须将其转换为numpy数组以适应数据框。然后,这个子数组就成为了numpy数组,并且需要进一步处理。请记住这一点,因为某些pandas函数可能无法在上面正常工作;在某些情况下,必须使用numpy函数。 newdf['xvalues']=newdf.apply(lambda row:np.array(df.x[(row.Amin<df.A) & (row...
Python program for binning a column with pandas # Importing pandas packageimportpandasaspd# Creating two dictionariesd1={'One':[iforiinrange(10,100,10)]}# Creating DataFramedf=pd.DataFrame(d1)# Display the DataFrameprint("Original DataFrame:\n",df,"\n")# Defining binsbins=[0,1,5,10,...
Keep in mind the values for the 25%, 50% and 75% percentiles as we look at using qcut directly. The simplest use of qcut is to define the number of quantiles and let pandas figure out how to divide up the data. In the example below, we tell pandas to create 4 equal sized grouping...
x: The input data, which can be a Pandas Series or a NumPy array. bins: This can be an integer value specifying the number of equal-width bins to create, or a sequence of scalar values defining the bin edges. If an integer is provided, the range of values in x will be divided int...
In this case, we choose two variables to discretized and the binary target. importpandasaspdfromsklearn.datasetsimportload_breast_cancerdata=load_breast_cancer()df=pd.DataFrame(data.data,columns=data.feature_names)variable1="mean radius"variable2="worst concavity"x=df[variable1].valuesy=df[varia...
Hrm, I had assumed it would've escalated toobjecttype for the array but left the actual values as strings and floats. I guess it's unlikely that someone will want to transform columns with different metrics, so maybe an exception is the best/easiest path forward. The message could suggest...