本文介绍了使用to_csv时如何保留数据帧的dtypes?的处理方法,对大家解决问题具有一定的参考价值,需要的朋友们下面随着小编来一起学习吧!
问题描述
为了减少内存成本,我使用astype()
指定了熊猫数据框的dtype,例如:
To reduce memory costs, I specified dtypes of my pandas dataframe using astype()
,like:
df['A'] = df['A'].astype(int8)
然后我使用to_csv()
来存储它,但是当我再次使用read_csv()
来读取它并检查dtypes
时,我发现它仍然存储在int64
中.将dtypes保存到本地存储时如何保存?
then I use to_csv()
to store it, but when I use read_csv()
to read it again and check the dtypes
, I found it still stored in int64
.How can I preserve the dtypes while saving it in local storages?
推荐答案
以下是 a 的实现方法:
import pandas as pd
# Create Example data with types
df = pd.DataFrame({
'words': ['foo', 'bar', 'spam', 'eggs'],
'nums': [1, 2, 3, 4]
}).astype(dtype={
'words': 'object',
'nums': 'int8'
})
def to_csv(df, path):
# Prepend dtypes to the top of df (from https://stackoverflow.com/a/43408736/7607701)
df.loc[-1] = df.dtypes
df.index = df.index + 1
df.sort_index(inplace=True)
# Then save it to a csv
df.to_csv(path, index=False)
def read_csv(path):
# Read types first line of csv
dtypes = pd.read_csv('tmp.csv', nrows=1).iloc[0].to_dict()
# Read the rest of the lines with the types from above
return pd.read_csv('tmp.csv', dtype=dtypes, skiprows=[1])
print('Before: \n{}\n'.format(df.dtypes))
to_csv(df, 'tmp.csv')
df = read_csv('tmp.csv')
print('After: \n{}\n'.format(df.dtypes))
输出:
Before:
nums int8
words object
dtype: object
After:
nums int8 # still int8
words object
dtype: object
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