如何导出和导入具有多索引行和列索引 into/from 的 pandas 数据框 csv 文件？
How can I export and import a pandas dataframe with multiindex row and column index into/from a csv file?
Pandas 允许使用 to_csv('path/to/file.csv'). However to me it is not clear how I can export (and import) a dataframe which uses MultiIndex for rows and columns like e.g. this one from the corresponding advanced docs:
将数据帧导出到 csv
first bar baz foo
second one two one two one two
first second
bar one -0.410001 -0.078638 0.545952 -1.219217 -1.226825 0.769804
two -1.281247 -0.727707 -0.121306 -0.097883 0.695775 0.341734
baz one 0.959726 -1.110336 -0.619976 0.149748 -0.732339 0.687738
two 0.176444 0.403310 -0.154951 0.301624 -2.179861 -1.369849
foo one -0.954208 1.462696 -1.743161 -0.826591 -0.345352 1.314232
two 0.690579 0.995761 2.396780 0.014871 3.357427 -0.317441
要在 Jupyter notebook 中使用随机数据值生成这样的数据帧：
import pandas as pd
import numpy as np
column_index_matrix = [np.array(['bar', 'bar', 'baz', 'baz', 'foo', 'foo']),
np.array(['one', 'two', 'one', 'two', 'one', 'two'])]
column_names = ['first', 'second']
column_multiindex = pd.MultiIndex.from_arrays(column_index_matrix, names=column_names)
column_multiindex
row_multiindex = column_multiindex
df = pd.DataFrame(np.random.randn(6, 6), index=row_multiindex, columns=column_multiindex)
df
WHen 运行 df.to_csv(r'df.csv', index=True) 数据框正确导出到 csv 文件。但是我不知道如何使用 pd.read_csv().
我之前尝试过，但最终认为最好是：
- 拆散整个列多索引
- reset_index
- to_csv
然后：
- read_csv
- pivot_table
看起来比较简单，但可能占用更多内存。
您可以传递多个 columns/rows 用作 index/header。在这里，我传递用于索引的第 0 列和第 1 列（因此两个级别）以及用于两个 header 级别的第 0 行和第 1 行：
pd.read_csv('data.csv', index_col=[0, 1], header=[0, 1])
first bar baz foo
second one two one two one two
first second
bar one 0.788793 -0.591498 -0.309037 -0.433105 -1.413536 -0.209560
two -0.354429 1.671837 1.527225 0.282775 -0.973088 -0.728555
baz one -0.180517 1.226219 -0.810984 -0.580251 -0.453205 -1.368015
two -0.040708 -0.836359 -2.043332 1.396955 -0.562718 -1.099926
foo one -0.612561 0.815998 -0.942997 -0.423395 0.157410 -0.537063
two -0.312878 0.194915 -1.420048 -0.944414 -0.560043 -0.036713
Pandas 允许使用 to_csv('path/to/file.csv'). However to me it is not clear how I can export (and import) a dataframe which uses MultiIndex for rows and columns like e.g. this one from the corresponding advanced docs:
first bar baz foo
second one two one two one two
first second
bar one -0.410001 -0.078638 0.545952 -1.219217 -1.226825 0.769804
two -1.281247 -0.727707 -0.121306 -0.097883 0.695775 0.341734
baz one 0.959726 -1.110336 -0.619976 0.149748 -0.732339 0.687738
two 0.176444 0.403310 -0.154951 0.301624 -2.179861 -1.369849
foo one -0.954208 1.462696 -1.743161 -0.826591 -0.345352 1.314232
two 0.690579 0.995761 2.396780 0.014871 3.357427 -0.317441
要在 Jupyter notebook 中使用随机数据值生成这样的数据帧：
import pandas as pd
import numpy as np
column_index_matrix = [np.array(['bar', 'bar', 'baz', 'baz', 'foo', 'foo']),
np.array(['one', 'two', 'one', 'two', 'one', 'two'])]
column_names = ['first', 'second']
column_multiindex = pd.MultiIndex.from_arrays(column_index_matrix, names=column_names)
column_multiindex
row_multiindex = column_multiindex
df = pd.DataFrame(np.random.randn(6, 6), index=row_multiindex, columns=column_multiindex)
df
WHen 运行 df.to_csv(r'df.csv', index=True) 数据框正确导出到 csv 文件。但是我不知道如何使用 pd.read_csv().
我之前尝试过，但最终认为最好是：
- 拆散整个列多索引
- reset_index
- to_csv
然后：
- read_csv
- pivot_table
看起来比较简单，但可能占用更多内存。
您可以传递多个 columns/rows 用作 index/header。在这里，我传递用于索引的第 0 列和第 1 列（因此两个级别）以及用于两个 header 级别的第 0 行和第 1 行：
pd.read_csv('data.csv', index_col=[0, 1], header=[0, 1])
first bar baz foo
second one two one two one two
first second
bar one 0.788793 -0.591498 -0.309037 -0.433105 -1.413536 -0.209560
two -0.354429 1.671837 1.527225 0.282775 -0.973088 -0.728555
baz one -0.180517 1.226219 -0.810984 -0.580251 -0.453205 -1.368015
two -0.040708 -0.836359 -2.043332 1.396955 -0.562718 -1.099926
foo one -0.612561 0.815998 -0.942997 -0.423395 0.157410 -0.537063
two -0.312878 0.194915 -1.420048 -0.944414 -0.560043 -0.036713