如果值同时是string/Integer数据类型，如何删除nan列？

import pandas as pd
import numpy as np

# 创建一个示例DataFrame
data = {
    'A': [1, 2, np.nan],
    'B': ['foo', 'bar', 'baz'],
    'C': [np.nan, np.nan, np.nan],
    'D': [4, 5, 6]
}
df = pd.DataFrame(data)

# 删除包含NaN值的列
df_cleaned = df.dropna(axis=1, how='any')

print(df_cleaned)

const _ = require('lodash');

// 创建一个示例对象
const data = {
    A: [1, 2, null],
    B: ['foo', 'bar', 'baz'],
    C: [null, null, null],
    D: [4, 5, 6]
};

// 删除包含NaN或null的列
const cleanedData = _.pickBy(data, col => !_.some(col, val => _.isNaN(val) || val === null));

console.log(cleanedData);

import org.apache.commons.math3.stat.StatUtils;
import java.util.Arrays;

public class Main {
    public static void main(String[] args) {
        double[][] data = {
            {1, Double.NaN, 3},
            {4, 5, Double.NaN},
            {7, 8, 9}
        };

        // 删除包含NaN的列
        double[][] cleanedData = removeNanColumns(data);

        for (double[] row : cleanedData) {
            System.out.println(Arrays.toString(row));
        }
    }

    private static double[][] removeNanColumns(double[][] data) {
        int numCols = data[0].length;
        boolean[] nanColumns = new boolean[numCols];

        for (int col = 0; col < numCols; col++) {
            for (double[] row : data) {
                if (Double.isNaN(row[col])) {
                    nanColumns[col] = true;
                    break;
                }
            }
        }

        int numCleanedCols = 0;
        for (boolean isNan : nanColumns) {
            if (!isNan) numCleanedCols++;
        }

        double[][] cleanedData = new double[data.length][numCleanedCols];
        int colIndex = 0;
        for (int i = 0; i < numCols; i++) {
            if (!nanColumns[i]) {
                for (int j = 0; j < data.length; j++) {
                    cleanedData[j][colIndex] = data[j][i];
                }
                colIndex++;
            }
        }

        return cleanedData;
    }
}