为 pandas.read_csv 指定正确的 dtypes 以获取日期时间和布尔值
问题描述
我正在将 csv 文件加载到 Pandas DataFrame 中.对于每一列，如何使用 dtype 参数指定它包含的数据类型?
I am loading a csv file into a Pandas DataFrame. For each column, how do I specify what type of data it contains using the dtype argument?
- 我可以使用 numeric 数据(代码在底部)...
- 但是如何指定时间数据...
- 和分类数据，例如因子或布尔值?我试过
np.bool_和pd.tslib.Timestamp没有运气.
- I can do it with numeric data (code at bottom)...
- But how do I specify time data...
- and categorical data such as factors or booleans? I have tried
np.bool_andpd.tslib.Timestampwithout luck.
代码:
import pandas as pd
import numpy as np
df = pd.read_csv(<file-name>, dtype={'A': np.int64, 'B': np.float64})
解决方案
read_csv 有很多选项可以处理你提到的所有情况.您可能想尝试 dtype={'A': datetime.datetime}，但通常您不需要 dtypes，因为 pandas 可以推断类型.
There are a lot of options for read_csv which will handle all the cases you mentioned. You might want to try dtype={'A': datetime.datetime}, but often you won't need dtypes as pandas can infer the types.
对于日期，则需要指定 parse_date 选项:
parse_dates : boolean, list of ints or names, list of lists, or dict
keep_date_col : boolean, default False
date_parser : function
一般来说，要转换布尔值，您需要指定:
true_values : list Values to consider as True
false_values : list Values to consider as False
这会将列表中的任何值转换为布尔值 true/false.对于更一般的转换，您很可能需要
Which will transform any value in the list to the boolean true/false. For more general conversions you will most likely need
转换器:字典.用于转换某些列中的值的可选函数字典.键可以是整数或列标签
converters : dict. optional Dict of functions for converting values in certain columns. Keys can either be integers or column labels
虽然密集，但请在此处查看完整列表:http://pandas.pydata.org/pandas-docs/stable/generated/pandas.io.parsers.read_csv.html
Though dense, check here for the full list: http://pandas.pydata.org/pandas-docs/stable/generated/pandas.io.parsers.read_csv.html
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