用字典合并两个熊猫数据框

Question

I have a dict that currently looks like this:

raw_data = {'Series_Date':['2017-03-10','2017-03-11','2017-03-12','2017-03-13','2017-03-14','2017-03-15'],'Value':[1,1,1,1,1,1]}
import pandas as pd
df1= pd.DataFrame(raw_data,columns=['Series_Date','Value'])
raw_data_ = {'Series_Date':['2017-03-16','2017-03-17','2017-03-18','2017-03-19','2017-03-20','2017-03-21'],'Value':[1,1,1,1,1,1]}
df2= pd.DataFrame(raw_data_,columns=['Series_Date','Value'])
dict = {}
dict = {'Check':df1, 'Check2': df2}
print dict

I want to find a way to merge the two dataframe series in my dict in such a manner that the key is the key of the first series and the values are the dataframes merged. My resulting dict should then look like:

raw_data = {'Series_Date':['2017-03-10','2017-03-11','2017-03-12','2017-03-13','2017-03-14','2017-03-15','2017-03-16','2017-03-17','2017-03-18','2017-03-19','2017-03-20','2017-03-21'],'Value':[1,1,1,1,1,1,1,1,1,1,1,1]}
import pandas as pd
df= pd.DataFrame(raw_data,columns=['Series_Date','Value'])
dict = {}
dict = {'Check':df}
print dict

Is there any simple way of doing this?

Answer 1

You can merge the two frames, and write over your dictionary.

df_merged = pd.concat(dict.values()).sort_values(by='Series_Date').reset_index(drop=True)
dict = {dict.keys()[0] : df_merged}

However the 'first' information is lost in a dictionary as the keys are not ordered. So the dict.keys()[0] will not necessarily give you the first data frame's key. You can use an OrderedDict to cope with this issue.

Then it would go like this:

raw_data = {'Series_Date':['2017-03-10','2017-03-11','2017-03-12','2017-03-13','2017-03-14','2017-03-15'],'Value':[1,1,1,1,1,1]}
import pandas as pd
from collections import OrderedDict
df1= pd.DataFrame(raw_data,columns=['Series_Date','Value'])
raw_data_ = {'Series_Date':['2017-03-16','2017-03-17','2017-03-18','2017-03-19','2017-03-20','2017-03-21'],'Value':[1,1,1,1,1,1]}
df2= pd.DataFrame(raw_data_,columns=['Series_Date','Value'])
dict = OrderedDict([('Check', df1), ('Check2', df2)])

df_merged = pd.concat(dict.values()).sort_values(by='Series_Date').reset_index(drop=True)
dict = {dict.keys()[0] : df_merged}