[英]Pandas melt dataframe in terms of date time and fill all values NaN
I have a dataframe including all countries and datetime ranging from "1/22/20" to "2/22/20".我有一个数据框,包括从“1/22/20”到“2/22/20”的所有国家和日期时间。
Here is my dataframe Column shown below.这是我的数据框列,如下所示。
Country 1/22/20 1/23/20 1/24/20 1/25/20 1/26/20 1/27/20 1/28/20 1/29/20 1/30/20...
I try to melt dataframe to get values in terms of datetime and country like我尝试融化数据框以获取日期时间和国家/地区方面的值
US 1/25/20 28
but all values defined as NaN
但所有值都定义为NaN
Australia 2020-01-22 NaN
How can I fix it?我该如何解决?
Here is my code snippet这是我的代码片段
def meltDataFrame(df,id_vars,value_vars,var_name,value_name):
return pd.melt(df,
id_vars= id_vars,
value_vars = value_vars,
var_name= var_name,
value_name= value_name)
data_df_melt = meltDataFrame(data_df.reset_index(),
['Country'],pd.date_range('1/22/20', '3/18/20', freq='D'),'Date','Total_Count')
Problem is columns names are not datetimes.问题是列名不是日期时间。
So convert all columns names without first to datetimes:因此,将所有没有 first 的列名称转换为日期时间:
df.columns = df.columns[:1].tolist() + pd.to_datetime(df.columns[1:]).tolist()
And then melt.然后融化。
Sample :样品:
print (df)
Country 1/22/20 1/23/20 1/24/20
0 Australia 11 42 53
df.columns = df.columns[:1].tolist() + pd.to_datetime(df.columns[1:]).tolist()
print (df)
Country 2020-01-22 00:00:00 2020-01-23 00:00:00 2020-01-24 00:00:00
0 Australia 11 42 53
声明:本站的技术帖子网页,遵循CC BY-SA 4.0协议,如果您需要转载,请注明本站网址或者原文地址。任何问题请咨询:yoyou2525@163.com.