常用的数据分析代码片段

本文会不定期更新一些在做数据分析时经常用到的技巧，以代码片段的形式提供，主要针对numpy，pandas和matplotlib等常用Python数据分析库。

1. 将原始数据中的日期时间字段转换为pandas.Timestamp类型

def convert_to_datetime(s):
  return pd.to_datetime(s.apply(lambda x: datetime.strptime(x, '%m/%d/%y %H:%M:%S')))

常见的日期格式说明符：

• %Y Four-digit year
• %y Two-digit year
• %m Two-digit month [01, 12]
• %d Two-digit day [01, 31]
• %H Hour (24-hour clock) [00, 23]
• %I Hour (12-hour clock) [01, 12]
• %M Two-digit minute [00, 59]
• %S Second [00, 61] (seconds 60, 61 account for leap seconds)
• %w Weekday as integer [0 (Sunday), 6]
• %U Week number of the year [00, 53]; Sunday is considered the frst day of the week, and days before the frst Sunday of the year are “week 0”
• %W Week number of the year [00, 53]; Monday is considered the frst day of the week, and days before the frst Monday of the year are “week 0”
• %z UTC time zone offset as +HHMM or -HHMM; empty if time zone naive
• %F Shortcut for %Y-%m-%d (e.g., 2012-4-18)
• %D Shortcut for %m/%d/%y (e.g., 04/18/12)

2. 将类别数据类型转换为category

raw_data['card_type'] = raw_data['card_type'].astype('category')