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Accepted
Roger V.
Asked: 2025-02-09 04:31:54 +0800 CST

Julia：相当于 Python 通过多索引级别（尤其是列）进行选择

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我对 Julia 和 Julia DataFrames 还不熟悉。我的理解是，DataFrames 不支持 MultiIndexing，这通常不会造成太大问题，但将一些 Python 习惯转换为 Julia 却很困难。我想知道如何按列加载和子选择特征，如下例所示。

import numpy as np
import pandas as pd

#generating sample data
nsmpls = 10
smpls = [f'smpl{j}' for j in range(nsmpls)]

nfeats = 5
feats = [f'feat{j}' for j in range(nfeats)]

data = np.random.rand(nfeats, nsmpls)

countries = ['France'] * 2 + ['UK'] * 3 + ['US'] * 5

df = pd.DataFrame(data, index=feats, columns=pd.MultiIndex.from_tuples(zip(countries, smpls)))
df.to_csv('./data.tsv', sep='\t')

#---------------------------------------------------------------------
#loading dataset
df = pd.read_csv('./data.tsv', sep='\t', index_col=0, header=[0,1])

#extracting subset
dg = df.xs('France', level=0, axis=1)
print(dg.shape)

#iterating
for country, group in df.groupby(level=0, axis=1):
    print('#samples: {}'.format(group.shape[1]))
python

1 个回答

  1. Best Answer

    像这样吗？

    using DataFrames, CSV

# Used your sample data
df = DataFrame(CSV.File("data.tsv"))

# Filter the columns by country name
france_cols = findall(x -> occursin("France", x), names(df))

# Subset the df
dg = select(df, france_cols)

# Optional : use "sampleX" as col names instead of the country name
rename!(dg, collect(dg[1, :]))
dg = dg[2:end, :]

display(dg)
println(size(dg))

    默认情况下，DataFrames 会将数字添加到类似的列名中，例如：France、France_1 等，因此我选择了所有包含“France”的列。

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