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?
np.bool_
和 pd.tslib.Timestamp
没有运气.np.bool_
and pd.tslib.Timestamp
without luck.代码:
import pandas as pd
import numpy as np
df = pd.read_csv(<file-name>, dtype={'A': np.int64, 'B': np.float64})
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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