python - 如何将 csv 数据文件导入 scikit-learn？

Question

据我了解，scikit-learn 接受 (n-sample, n-feature) 格式的数据，这是一个 2D 数组。假设我有表单中的数据...

Stock prices    indicator1    indicator2
2.0             123           1252
1.0             ..            ..
..              .             . 
.

如何导入？

Answer 1

numpy loadtxt 的一个很好的替代品是 read_csv from Pandas .数据被加载到 Pandas 数据帧中的一大优势是它可以处理混合数据类型，例如某些列包含文本，而其他列包含数字。然后，您可以轻松地仅选择数字列并使用 as_matrix 转换为 numpy 数组。 . Pandas 也会read/write excel files and a bunch of other formats .

如果我们有一个名为“mydata.csv”的 csv 文件:

point_latitude,point_longitude,line,construction,point_granularity
30.102261, -81.711777, Residential, Masonry, 1
30.063936, -81.707664, Residential, Masonry, 3
30.089579, -81.700455, Residential, Wood   , 1
30.063236, -81.707703, Residential, Wood   , 3
30.060614, -81.702675, Residential, Wood   , 1

这将读入 csv 并将数字列转换为 scikit_learn 的 numpy 数组，然后修改列的顺序并将其写入 excel 电子表格:

import numpy as np
import pandas as pd

input_file = "mydata.csv"


# comma delimited is the default
df = pd.read_csv(input_file, header = 0)

# for space delimited use:
# df = pd.read_csv(input_file, header = 0, delimiter = " ")

# for tab delimited use:
# df = pd.read_csv(input_file, header = 0, delimiter = "\t")

# put the original column names in a python list
original_headers = list(df.columns.values)

# remove the non-numeric columns
df = df._get_numeric_data()

# put the numeric column names in a python list
numeric_headers = list(df.columns.values)

# create a numpy array with the numeric values for input into scikit-learn
numpy_array = df.as_matrix()

# reverse the order of the columns
numeric_headers.reverse()
reverse_df = df[numeric_headers]

# write the reverse_df to an excel spreadsheet
reverse_df.to_excel('path_to_file.xls')