Build new dataframe with for loop given condition in another dataframe












0















This is the dataframe I want to iterate through. The index is set for both countries and year.



                            ISO_code    gini    ECONOMIC FREEDOM    rank    quartile    1a_government_consumption
countries year
Argentina 1980 ARG 40.8 4.25 80.0 4.0 6.911765
1995 ARG 48.9 6.95 37.0 2.0 8.058824
2000 ARG 51.1 7.34 37.0 2.0 6.877627
2001 ARG 53.3 6.84 56.0 2.0 6.752473
2002 ARG 53.8 6.28 79.0 3.0 6.905961
2003 ARG 50.7 6.16 86.0 3.0 7.264992
Bolivia 1980 BOL 40.8 4.25 80.0 4.0 6.911765
1985 BOL 48.9 6.95 37.0 2.0 8.058824
1995 BOL 51.1 7.34 37.0 2.0 6.877627
2000 BOL 53.3 6.84 56.0 2.0 6.752473
2001 BOL 53.8 6.28 79.0 3.0 6.905961
2002 BOL 50.7 6.16 86.0 3.0 7.264992
Chile 1985 CHI 40.8 4.25 80.0 4.0 6.911765
1990 CHI 48.9 6.95 37.0 2.0 8.058824
1995 CHI 51.1 7.34 37.0 2.0 6.877627
1999 CHI 53.3 6.84 56.0 2.0 6.752473
2002 CHI 53.8 6.28 79.0 3.0 6.905961
2003 CHI 50.7 6.16 86.0 3.0 7.264992


I would like to create a for loop that returns a dataframe like this one:



countries    change gini    change ef                                                                 
Argentina + +
Bolivia - +
Chile - -



  1. countries is simply the columns with the country names from the previous dataframe.


  2. change gini should be the percentage difference between the last value of the gini column for each country and the most recent one. If the percentage increase is positive, then it should show a +; if it is negativa then it should show a -.


  3. change ef follows the same logic as the change gini in the new dataframe, with the only difference that the values used for calculating the percentage change come from the ECONOMIC FREEDOM column in the original dataframe.











share|improve this question



























    0















    This is the dataframe I want to iterate through. The index is set for both countries and year.



                                ISO_code    gini    ECONOMIC FREEDOM    rank    quartile    1a_government_consumption
    countries year
    Argentina 1980 ARG 40.8 4.25 80.0 4.0 6.911765
    1995 ARG 48.9 6.95 37.0 2.0 8.058824
    2000 ARG 51.1 7.34 37.0 2.0 6.877627
    2001 ARG 53.3 6.84 56.0 2.0 6.752473
    2002 ARG 53.8 6.28 79.0 3.0 6.905961
    2003 ARG 50.7 6.16 86.0 3.0 7.264992
    Bolivia 1980 BOL 40.8 4.25 80.0 4.0 6.911765
    1985 BOL 48.9 6.95 37.0 2.0 8.058824
    1995 BOL 51.1 7.34 37.0 2.0 6.877627
    2000 BOL 53.3 6.84 56.0 2.0 6.752473
    2001 BOL 53.8 6.28 79.0 3.0 6.905961
    2002 BOL 50.7 6.16 86.0 3.0 7.264992
    Chile 1985 CHI 40.8 4.25 80.0 4.0 6.911765
    1990 CHI 48.9 6.95 37.0 2.0 8.058824
    1995 CHI 51.1 7.34 37.0 2.0 6.877627
    1999 CHI 53.3 6.84 56.0 2.0 6.752473
    2002 CHI 53.8 6.28 79.0 3.0 6.905961
    2003 CHI 50.7 6.16 86.0 3.0 7.264992


    I would like to create a for loop that returns a dataframe like this one:



    countries    change gini    change ef                                                                 
    Argentina + +
    Bolivia - +
    Chile - -



    1. countries is simply the columns with the country names from the previous dataframe.


    2. change gini should be the percentage difference between the last value of the gini column for each country and the most recent one. If the percentage increase is positive, then it should show a +; if it is negativa then it should show a -.


    3. change ef follows the same logic as the change gini in the new dataframe, with the only difference that the values used for calculating the percentage change come from the ECONOMIC FREEDOM column in the original dataframe.











    share|improve this question

























      0












      0








      0








      This is the dataframe I want to iterate through. The index is set for both countries and year.



                                  ISO_code    gini    ECONOMIC FREEDOM    rank    quartile    1a_government_consumption
      countries year
      Argentina 1980 ARG 40.8 4.25 80.0 4.0 6.911765
      1995 ARG 48.9 6.95 37.0 2.0 8.058824
      2000 ARG 51.1 7.34 37.0 2.0 6.877627
      2001 ARG 53.3 6.84 56.0 2.0 6.752473
      2002 ARG 53.8 6.28 79.0 3.0 6.905961
      2003 ARG 50.7 6.16 86.0 3.0 7.264992
      Bolivia 1980 BOL 40.8 4.25 80.0 4.0 6.911765
      1985 BOL 48.9 6.95 37.0 2.0 8.058824
      1995 BOL 51.1 7.34 37.0 2.0 6.877627
      2000 BOL 53.3 6.84 56.0 2.0 6.752473
      2001 BOL 53.8 6.28 79.0 3.0 6.905961
      2002 BOL 50.7 6.16 86.0 3.0 7.264992
      Chile 1985 CHI 40.8 4.25 80.0 4.0 6.911765
      1990 CHI 48.9 6.95 37.0 2.0 8.058824
      1995 CHI 51.1 7.34 37.0 2.0 6.877627
      1999 CHI 53.3 6.84 56.0 2.0 6.752473
      2002 CHI 53.8 6.28 79.0 3.0 6.905961
      2003 CHI 50.7 6.16 86.0 3.0 7.264992


      I would like to create a for loop that returns a dataframe like this one:



      countries    change gini    change ef                                                                 
      Argentina + +
      Bolivia - +
      Chile - -



      1. countries is simply the columns with the country names from the previous dataframe.


      2. change gini should be the percentage difference between the last value of the gini column for each country and the most recent one. If the percentage increase is positive, then it should show a +; if it is negativa then it should show a -.


      3. change ef follows the same logic as the change gini in the new dataframe, with the only difference that the values used for calculating the percentage change come from the ECONOMIC FREEDOM column in the original dataframe.











      share|improve this question














      This is the dataframe I want to iterate through. The index is set for both countries and year.



                                  ISO_code    gini    ECONOMIC FREEDOM    rank    quartile    1a_government_consumption
      countries year
      Argentina 1980 ARG 40.8 4.25 80.0 4.0 6.911765
      1995 ARG 48.9 6.95 37.0 2.0 8.058824
      2000 ARG 51.1 7.34 37.0 2.0 6.877627
      2001 ARG 53.3 6.84 56.0 2.0 6.752473
      2002 ARG 53.8 6.28 79.0 3.0 6.905961
      2003 ARG 50.7 6.16 86.0 3.0 7.264992
      Bolivia 1980 BOL 40.8 4.25 80.0 4.0 6.911765
      1985 BOL 48.9 6.95 37.0 2.0 8.058824
      1995 BOL 51.1 7.34 37.0 2.0 6.877627
      2000 BOL 53.3 6.84 56.0 2.0 6.752473
      2001 BOL 53.8 6.28 79.0 3.0 6.905961
      2002 BOL 50.7 6.16 86.0 3.0 7.264992
      Chile 1985 CHI 40.8 4.25 80.0 4.0 6.911765
      1990 CHI 48.9 6.95 37.0 2.0 8.058824
      1995 CHI 51.1 7.34 37.0 2.0 6.877627
      1999 CHI 53.3 6.84 56.0 2.0 6.752473
      2002 CHI 53.8 6.28 79.0 3.0 6.905961
      2003 CHI 50.7 6.16 86.0 3.0 7.264992


      I would like to create a for loop that returns a dataframe like this one:



      countries    change gini    change ef                                                                 
      Argentina + +
      Bolivia - +
      Chile - -



      1. countries is simply the columns with the country names from the previous dataframe.


      2. change gini should be the percentage difference between the last value of the gini column for each country and the most recent one. If the percentage increase is positive, then it should show a +; if it is negativa then it should show a -.


      3. change ef follows the same logic as the change gini in the new dataframe, with the only difference that the values used for calculating the percentage change come from the ECONOMIC FREEDOM column in the original dataframe.








      python pandas loops dataframe for-loop






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      asked Nov 22 '18 at 0:50









      Guillermina Sutter SchneiderGuillermina Sutter Schneider

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          2 Answers
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          1














          You can achieve this quite easily via grouping functions.

          Unfortunately the first and last values of the three countries in your dataset are the same, so the result is also three times the same two values.
          (Perhaps there is sth wrong with the sample data?)



          First group the dataframe by countries and pick just the two columns of interest:



          grpd = df.groupby('countries')['gini', 'ECONOMIC FREEDOM']


          With this Groupby-Object you can apply functions to the the subsets of your data which are separated by the grouping feature, countries in your case.

          E.g. to get the last value in each group just ask for



          grpd.last()

          gini ECONOMIC FREEDOM
          countries
          Argentina 50.7 6.16
          Bolivia 50.7 6.16
          Chile 50.7 6.16


          or accordingly for the first row per group



          grpd.first()

          gini ECONOMIC FREEDOM
          countries
          Argentina 40.8 4.25
          Bolivia 40.8 4.25
          Chile 40.8 4.25


          for calculating the percentage of change of the last with respect to the first you could therefore simply write



          (grpd.last() - grpd.first()) / grpd.first()

          gini ECONOMIC FREEDOM
          countries
          Argentina 0.242647 0.449411
          Bolivia 0.242647 0.449411
          Chile 0.242647 0.449411


          EDIT:

          the output can also be formatted, e.g. like:



          df_change = (grpd.last() - grpd.first()) / grpd.first()

          df_change.applymap(lambda x: str.format('{:+.1f%}', x))

          gini ECONOMIC FREEDOM
          countries
          Argentina +24.3% +44.9%
          Bolivia +24.3% +44.9%
          Chile +24.3% +44.9%




          EDIT2:

          for signs only:



          df_change.applymap(lambda x: ['-', ' ', '+'][np.sign(x).astype(int)+1])

          gini ECONOMIC FREEDOM
          countries
          Argentina + +
          Bolivia + +
          Chile + +





          share|improve this answer

































            0














            Create empty lists and append desired values from countries, gini, and ECONOMIC FREEDOM columns for each country.



                countries = 
            gini =
            efw =
            for i in new_df.index.levels[0]:
            countries.append(i)
            country = new_df.loc[i]
            country = country.reset_index()
            x = country.iloc[0].tolist()
            y = country.iloc[-1].tolist()
            change_g = (((y[2] / x[2]) - 1) * 100)
            change_e = (((y[3] / x[3]) - 1) * 100)
            gini.append(change_g)
            efw.append(change_e)


            Then do a for loop. For each number you append a + or a -.



            g = 
            e =
            for n in gini:
            if n > 0:
            g.append("+")
            g.append("-")

            for f in efw:
            if f > 0:
            e.append("+")
            e.append("-")


            Then create a dataframe with the lists countries, g, and e.



            tuples = list(zip(countries,g,e))
            changes = pd.DataFrame(tuples, columns=['Country','Change in Gini', "Change in Economic Freedom"])





            share|improve this answer
























            • This reads more like general purpose Python and not pandas-style Python. Consider groupby and vectorized (non-loop) processing.

              – Parfait
              Nov 23 '18 at 1:26











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            2 Answers
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            active

            oldest

            votes








            2 Answers
            2






            active

            oldest

            votes









            active

            oldest

            votes






            active

            oldest

            votes









            1














            You can achieve this quite easily via grouping functions.

            Unfortunately the first and last values of the three countries in your dataset are the same, so the result is also three times the same two values.
            (Perhaps there is sth wrong with the sample data?)



            First group the dataframe by countries and pick just the two columns of interest:



            grpd = df.groupby('countries')['gini', 'ECONOMIC FREEDOM']


            With this Groupby-Object you can apply functions to the the subsets of your data which are separated by the grouping feature, countries in your case.

            E.g. to get the last value in each group just ask for



            grpd.last()

            gini ECONOMIC FREEDOM
            countries
            Argentina 50.7 6.16
            Bolivia 50.7 6.16
            Chile 50.7 6.16


            or accordingly for the first row per group



            grpd.first()

            gini ECONOMIC FREEDOM
            countries
            Argentina 40.8 4.25
            Bolivia 40.8 4.25
            Chile 40.8 4.25


            for calculating the percentage of change of the last with respect to the first you could therefore simply write



            (grpd.last() - grpd.first()) / grpd.first()

            gini ECONOMIC FREEDOM
            countries
            Argentina 0.242647 0.449411
            Bolivia 0.242647 0.449411
            Chile 0.242647 0.449411


            EDIT:

            the output can also be formatted, e.g. like:



            df_change = (grpd.last() - grpd.first()) / grpd.first()

            df_change.applymap(lambda x: str.format('{:+.1f%}', x))

            gini ECONOMIC FREEDOM
            countries
            Argentina +24.3% +44.9%
            Bolivia +24.3% +44.9%
            Chile +24.3% +44.9%




            EDIT2:

            for signs only:



            df_change.applymap(lambda x: ['-', ' ', '+'][np.sign(x).astype(int)+1])

            gini ECONOMIC FREEDOM
            countries
            Argentina + +
            Bolivia + +
            Chile + +





            share|improve this answer






























              1














              You can achieve this quite easily via grouping functions.

              Unfortunately the first and last values of the three countries in your dataset are the same, so the result is also three times the same two values.
              (Perhaps there is sth wrong with the sample data?)



              First group the dataframe by countries and pick just the two columns of interest:



              grpd = df.groupby('countries')['gini', 'ECONOMIC FREEDOM']


              With this Groupby-Object you can apply functions to the the subsets of your data which are separated by the grouping feature, countries in your case.

              E.g. to get the last value in each group just ask for



              grpd.last()

              gini ECONOMIC FREEDOM
              countries
              Argentina 50.7 6.16
              Bolivia 50.7 6.16
              Chile 50.7 6.16


              or accordingly for the first row per group



              grpd.first()

              gini ECONOMIC FREEDOM
              countries
              Argentina 40.8 4.25
              Bolivia 40.8 4.25
              Chile 40.8 4.25


              for calculating the percentage of change of the last with respect to the first you could therefore simply write



              (grpd.last() - grpd.first()) / grpd.first()

              gini ECONOMIC FREEDOM
              countries
              Argentina 0.242647 0.449411
              Bolivia 0.242647 0.449411
              Chile 0.242647 0.449411


              EDIT:

              the output can also be formatted, e.g. like:



              df_change = (grpd.last() - grpd.first()) / grpd.first()

              df_change.applymap(lambda x: str.format('{:+.1f%}', x))

              gini ECONOMIC FREEDOM
              countries
              Argentina +24.3% +44.9%
              Bolivia +24.3% +44.9%
              Chile +24.3% +44.9%




              EDIT2:

              for signs only:



              df_change.applymap(lambda x: ['-', ' ', '+'][np.sign(x).astype(int)+1])

              gini ECONOMIC FREEDOM
              countries
              Argentina + +
              Bolivia + +
              Chile + +





              share|improve this answer




























                1












                1








                1







                You can achieve this quite easily via grouping functions.

                Unfortunately the first and last values of the three countries in your dataset are the same, so the result is also three times the same two values.
                (Perhaps there is sth wrong with the sample data?)



                First group the dataframe by countries and pick just the two columns of interest:



                grpd = df.groupby('countries')['gini', 'ECONOMIC FREEDOM']


                With this Groupby-Object you can apply functions to the the subsets of your data which are separated by the grouping feature, countries in your case.

                E.g. to get the last value in each group just ask for



                grpd.last()

                gini ECONOMIC FREEDOM
                countries
                Argentina 50.7 6.16
                Bolivia 50.7 6.16
                Chile 50.7 6.16


                or accordingly for the first row per group



                grpd.first()

                gini ECONOMIC FREEDOM
                countries
                Argentina 40.8 4.25
                Bolivia 40.8 4.25
                Chile 40.8 4.25


                for calculating the percentage of change of the last with respect to the first you could therefore simply write



                (grpd.last() - grpd.first()) / grpd.first()

                gini ECONOMIC FREEDOM
                countries
                Argentina 0.242647 0.449411
                Bolivia 0.242647 0.449411
                Chile 0.242647 0.449411


                EDIT:

                the output can also be formatted, e.g. like:



                df_change = (grpd.last() - grpd.first()) / grpd.first()

                df_change.applymap(lambda x: str.format('{:+.1f%}', x))

                gini ECONOMIC FREEDOM
                countries
                Argentina +24.3% +44.9%
                Bolivia +24.3% +44.9%
                Chile +24.3% +44.9%




                EDIT2:

                for signs only:



                df_change.applymap(lambda x: ['-', ' ', '+'][np.sign(x).astype(int)+1])

                gini ECONOMIC FREEDOM
                countries
                Argentina + +
                Bolivia + +
                Chile + +





                share|improve this answer















                You can achieve this quite easily via grouping functions.

                Unfortunately the first and last values of the three countries in your dataset are the same, so the result is also three times the same two values.
                (Perhaps there is sth wrong with the sample data?)



                First group the dataframe by countries and pick just the two columns of interest:



                grpd = df.groupby('countries')['gini', 'ECONOMIC FREEDOM']


                With this Groupby-Object you can apply functions to the the subsets of your data which are separated by the grouping feature, countries in your case.

                E.g. to get the last value in each group just ask for



                grpd.last()

                gini ECONOMIC FREEDOM
                countries
                Argentina 50.7 6.16
                Bolivia 50.7 6.16
                Chile 50.7 6.16


                or accordingly for the first row per group



                grpd.first()

                gini ECONOMIC FREEDOM
                countries
                Argentina 40.8 4.25
                Bolivia 40.8 4.25
                Chile 40.8 4.25


                for calculating the percentage of change of the last with respect to the first you could therefore simply write



                (grpd.last() - grpd.first()) / grpd.first()

                gini ECONOMIC FREEDOM
                countries
                Argentina 0.242647 0.449411
                Bolivia 0.242647 0.449411
                Chile 0.242647 0.449411


                EDIT:

                the output can also be formatted, e.g. like:



                df_change = (grpd.last() - grpd.first()) / grpd.first()

                df_change.applymap(lambda x: str.format('{:+.1f%}', x))

                gini ECONOMIC FREEDOM
                countries
                Argentina +24.3% +44.9%
                Bolivia +24.3% +44.9%
                Chile +24.3% +44.9%




                EDIT2:

                for signs only:



                df_change.applymap(lambda x: ['-', ' ', '+'][np.sign(x).astype(int)+1])

                gini ECONOMIC FREEDOM
                countries
                Argentina + +
                Bolivia + +
                Chile + +






                share|improve this answer














                share|improve this answer



                share|improve this answer








                edited Nov 22 '18 at 23:48

























                answered Nov 22 '18 at 6:14









                SpghttCdSpghttCd

                4,6822313




                4,6822313

























                    0














                    Create empty lists and append desired values from countries, gini, and ECONOMIC FREEDOM columns for each country.



                        countries = 
                    gini =
                    efw =
                    for i in new_df.index.levels[0]:
                    countries.append(i)
                    country = new_df.loc[i]
                    country = country.reset_index()
                    x = country.iloc[0].tolist()
                    y = country.iloc[-1].tolist()
                    change_g = (((y[2] / x[2]) - 1) * 100)
                    change_e = (((y[3] / x[3]) - 1) * 100)
                    gini.append(change_g)
                    efw.append(change_e)


                    Then do a for loop. For each number you append a + or a -.



                    g = 
                    e =
                    for n in gini:
                    if n > 0:
                    g.append("+")
                    g.append("-")

                    for f in efw:
                    if f > 0:
                    e.append("+")
                    e.append("-")


                    Then create a dataframe with the lists countries, g, and e.



                    tuples = list(zip(countries,g,e))
                    changes = pd.DataFrame(tuples, columns=['Country','Change in Gini', "Change in Economic Freedom"])





                    share|improve this answer
























                    • This reads more like general purpose Python and not pandas-style Python. Consider groupby and vectorized (non-loop) processing.

                      – Parfait
                      Nov 23 '18 at 1:26
















                    0














                    Create empty lists and append desired values from countries, gini, and ECONOMIC FREEDOM columns for each country.



                        countries = 
                    gini =
                    efw =
                    for i in new_df.index.levels[0]:
                    countries.append(i)
                    country = new_df.loc[i]
                    country = country.reset_index()
                    x = country.iloc[0].tolist()
                    y = country.iloc[-1].tolist()
                    change_g = (((y[2] / x[2]) - 1) * 100)
                    change_e = (((y[3] / x[3]) - 1) * 100)
                    gini.append(change_g)
                    efw.append(change_e)


                    Then do a for loop. For each number you append a + or a -.



                    g = 
                    e =
                    for n in gini:
                    if n > 0:
                    g.append("+")
                    g.append("-")

                    for f in efw:
                    if f > 0:
                    e.append("+")
                    e.append("-")


                    Then create a dataframe with the lists countries, g, and e.



                    tuples = list(zip(countries,g,e))
                    changes = pd.DataFrame(tuples, columns=['Country','Change in Gini', "Change in Economic Freedom"])





                    share|improve this answer
























                    • This reads more like general purpose Python and not pandas-style Python. Consider groupby and vectorized (non-loop) processing.

                      – Parfait
                      Nov 23 '18 at 1:26














                    0












                    0








                    0







                    Create empty lists and append desired values from countries, gini, and ECONOMIC FREEDOM columns for each country.



                        countries = 
                    gini =
                    efw =
                    for i in new_df.index.levels[0]:
                    countries.append(i)
                    country = new_df.loc[i]
                    country = country.reset_index()
                    x = country.iloc[0].tolist()
                    y = country.iloc[-1].tolist()
                    change_g = (((y[2] / x[2]) - 1) * 100)
                    change_e = (((y[3] / x[3]) - 1) * 100)
                    gini.append(change_g)
                    efw.append(change_e)


                    Then do a for loop. For each number you append a + or a -.



                    g = 
                    e =
                    for n in gini:
                    if n > 0:
                    g.append("+")
                    g.append("-")

                    for f in efw:
                    if f > 0:
                    e.append("+")
                    e.append("-")


                    Then create a dataframe with the lists countries, g, and e.



                    tuples = list(zip(countries,g,e))
                    changes = pd.DataFrame(tuples, columns=['Country','Change in Gini', "Change in Economic Freedom"])





                    share|improve this answer













                    Create empty lists and append desired values from countries, gini, and ECONOMIC FREEDOM columns for each country.



                        countries = 
                    gini =
                    efw =
                    for i in new_df.index.levels[0]:
                    countries.append(i)
                    country = new_df.loc[i]
                    country = country.reset_index()
                    x = country.iloc[0].tolist()
                    y = country.iloc[-1].tolist()
                    change_g = (((y[2] / x[2]) - 1) * 100)
                    change_e = (((y[3] / x[3]) - 1) * 100)
                    gini.append(change_g)
                    efw.append(change_e)


                    Then do a for loop. For each number you append a + or a -.



                    g = 
                    e =
                    for n in gini:
                    if n > 0:
                    g.append("+")
                    g.append("-")

                    for f in efw:
                    if f > 0:
                    e.append("+")
                    e.append("-")


                    Then create a dataframe with the lists countries, g, and e.



                    tuples = list(zip(countries,g,e))
                    changes = pd.DataFrame(tuples, columns=['Country','Change in Gini', "Change in Economic Freedom"])






                    share|improve this answer












                    share|improve this answer



                    share|improve this answer










                    answered Nov 22 '18 at 2:15









                    Guillermina Sutter SchneiderGuillermina Sutter Schneider

                    11511




                    11511













                    • This reads more like general purpose Python and not pandas-style Python. Consider groupby and vectorized (non-loop) processing.

                      – Parfait
                      Nov 23 '18 at 1:26



















                    • This reads more like general purpose Python and not pandas-style Python. Consider groupby and vectorized (non-loop) processing.

                      – Parfait
                      Nov 23 '18 at 1:26

















                    This reads more like general purpose Python and not pandas-style Python. Consider groupby and vectorized (non-loop) processing.

                    – Parfait
                    Nov 23 '18 at 1:26





                    This reads more like general purpose Python and not pandas-style Python. Consider groupby and vectorized (non-loop) processing.

                    – Parfait
                    Nov 23 '18 at 1:26


















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