convert_a_dictionary_of_dataframes_to_a_big_dataframe []

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===== Convert a dictionary of dataframes to a big dataframe =====
==== Task ====
Given a dictionary of strings to dataframes, create an expanded dataframe by putting the keys into their own column. For example, given
<code>
{'COST':    fiscal_quarter_end  reported_date
 0              202102       20210304
 1              202105       20210527
 2              202108       20210923
 3              202111       20211209,
 'CPRT':    fiscal_quarter_end  reported_date
 0              202104       20210519
 1              202107       20210908
 2              202110       20211117
 3              202201       20220216}
</code>
We want
<code>
  ticker  fiscal_quarter_end  reported_date
0   COST              202102       20210304
1   COST              202105       20210527
2   COST              202108       20210923
3   COST              202111       20211209
4   CPRT              202104       20210519
5   CPRT              202107       20210908
6   CPRT              202110       20211117
7   CPRT              202201       20220216
</code>

==== Solution ====
<code>
def dict_df_to_df(dict_df, key_col_name):
    df = (
        pd.concat(dict_df, axis=0)\
        .reset_index()\
        .drop('level_1', axis=1)\
        .rename({'level_0':key_col_name}, axis=1)
    )
    return df
</code>
Using the example above
<code>
$ ipython

In [1]:
import pandas as pd
costco_earnings = pd.DataFrame({
  'fiscal_quarter_end': [202102, 202105, 202108, 202111],
  'reported_date': [20210304, 20210527, 20210923, 20211209]
})
costco_earnings
Out[1]:
   fiscal_quarter_end  reported_date
0              202102       20210304
1              202105       20210527
2              202108       20210923
3              202111       20211209

In [2]:
copart_earnings = pd.DataFrame({
  'fiscal_quarter_end': [202104, 202107, 202110, 202201],
  'reported_date': [20210519, 20210908, 20211117, 20220216]
})
copart_earnings
Out[2]:
   fiscal_quarter_end  reported_date
0              202104       20210519
1              202107       20210908
2              202110       20211117
3              202201       20220216

In [3]:
dict_df = {'COST': costco_earnings, 'CPRT': copart_earnings}
dict_df
Out[3]:
{'COST':    fiscal_quarter_end  reported_date
 0              202102       20210304
 1              202105       20210527
 2              202108       20210923
 3              202111       20211209,
 'CPRT':    fiscal_quarter_end  reported_date
 0              202104       20210519
 1              202107       20210908
 2              202110       20211117
 3              202201       20220216}
</code>

<code>
In [4]:
def dict_df_to_df(dict_df, key_col_name):
    df = (
        pd.concat(dict_df, axis=0)\
        .reset_index()\
        .drop('level_1', axis=1)\
        .rename({'level_0':key_col_name}, axis=1)
    )
    return df
</code>

<code>
In [5]:
expanded_df = dict_df_to_df(dict_df, 'ticker')
expanded_df
Out[5]:
  ticker  fiscal_quarter_end  reported_date
0   COST              202102       20210304
1   COST              202105       20210527
2   COST              202108       20210923
3   COST              202111       20211209
4   CPRT              202104       20210519
5   CPRT              202107       20210908
6   CPRT              202110       20211117
7   CPRT              202201       20220216
</code>

To see how it works
<code>
In [6]:
pd.concat(dict_df, axis=0)
Out[6]:
        fiscal_quarter_end  reported_date
COST 0              202102       20210304
     1              202105       20210527
     2              202108       20210923
     3              202111       20211209
CPRT 0              202104       20210519
     1              202107       20210908
     2              202110       20211117
     3              202201       20220216

In [7]:
pd.concat(dict_df, axis=0)\
.reset_index()
Out[7]:
  level_0  level_1  fiscal_quarter_end  reported_date
0    COST        0              202102       20210304
1    COST        1              202105       20210527
2    COST        2              202108       20210923
3    COST        3              202111       20211209
4    CPRT        0              202104       20210519
5    CPRT        1              202107       20210908
6    CPRT        2              202110       20211117
7    CPRT        3              202201       20220216

In [8]:
pd.concat(dict_df, axis=0)\
.reset_index()\
.drop('level_1', axis=1)
Out[8]:
  level_0  fiscal_quarter_end  reported_date
0    COST              202102       20210304
1    COST              202105       20210527
2    COST              202108       20210923
3    COST              202111       20211209
4    CPRT              202104       20210519
5    CPRT              202107       20210908
6    CPRT              202110       20211117
7    CPRT              202201       20220216

In [9]:
pd.concat(dict_df, axis=0)\
.reset_index()\
.drop('level_1', axis=1)\
.rename({'level_0':'ticker'}, axis=1)
Out[9]:
  ticker  fiscal_quarter_end  reported_date
0   COST              202102       20210304
1   COST              202105       20210527
2   COST              202108       20210923
3   COST              202111       20211209
4   CPRT              202104       20210519
5   CPRT              202107       20210908
6   CPRT              202110       20211117
7   CPRT              202201       20220216
</code>

==== Ref ====
  * https://stackoverflow.com/questions/56638413/how-to-create-a-pandas-dataframe-from-dictionary-of-dataframes - where I first came across this solution. I just put the solution in a function, used a better example and showed some intermediate steps on how the solution works.