是否有可能以尊重分层列结构的方式通过 csv 来回传输 DataFrame?换句话说,如果我有以下 DataFrame:
>>> cols = pd.MultiIndex.from_arrays([["foo", "foo", "bar", "bar"],
["a", "b", "c", "d"]])
>>> df = pd.DataFrame(np.random.randn(5, 4), index=range(5), columns=cols)
执行以下操作失败:
>>> df.to_csv("df.csv", index_label="index")
>>> df_new = pd.read_csv("df.csv", index_col="index")
>>> assert df.columns == df_new.columns
我是否遗漏了 csv 保存/读取步骤中的某些选项?
最佳答案
在特殊情况下,您有一个列式 MultiIndex,但索引很简单,您可以转置 DataFrame 并使用 index_label
和 index_col
,如下所示:
import numpy as np
import pandas as pd
cols = pd.MultiIndex.from_arrays([["foo", "foo", "bar", "bar"],
["a", "b", "c", "d"]])
df = pd.DataFrame(np.random.randn(5, 4), index=range(5), columns=cols)
(df.T).to_csv('/tmp/df.csv', index_label=['first','second'])
df_new = pd.read_csv('/tmp/df.csv', index_col=['first','second']).T
assert np.all(df.columns.values == df_new.columns.values)
但不幸的是,这引出了一个问题,如果索引和列都是多索引该怎么办?
这是一个 hacky 解决方法:
import numpy as np
import pandas as pd
import ast
cols = pd.MultiIndex.from_arrays([["foo", "foo", "bar", "bar"],
["a", "b", "c", "d"]])
df = pd.DataFrame(np.random.randn(5, 4), index=range(5), columns=cols)
print(df)
df.to_csv('/tmp/df.csv', index_label='index')
df_new = pd.read_csv('/tmp/df.csv', index_col='index')
columns = pd.MultiIndex.from_tuples([ast.literal_eval(item) for item in df_new.columns])
df_new.columns = columns
df_new.index.name = None
print(df_new)
assert np.all(df.columns.values == df_new.columns.values)
当然,如果你只是想将DataFrame存储在任何格式的文件中,那么df.save
和pd.load
提供了一个更愉快的解决方案:
import numpy as np
import pandas as pd
cols = pd.MultiIndex.from_arrays([["foo", "foo", "bar", "bar"],
["a", "b", "c", "d"]])
df = pd.DataFrame(np.random.randn(5, 4), index=range(5), columns=cols)
df.save('/tmp/df.df')
df_new = pd.load('/tmp/df.df')
assert np.all(df.columns.values == df_new.columns.values)
关于python - Pandas 分层列和 csv 函数,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/16389097/