How to properly use groupby on pandas











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I started to work in a startup, but I'm the only data analyst, so I can't ask in my company, my problem is that in a lot of parts of my code I have to divide the dataframe, so I use the groupby to:



for name, group in mvtos_material_df.groupby('Referencia'):
# Work with the groups


But I think that working with for loops on pandas is wrong, and for some easy functions I saw that the idea is to use groupby().apply() but I have also the next code which I don't how to fix it, if not putting it all in a method, but seems a wrong idea.



moves_dict = {}

for name, group in mvtos_material_df.groupby('Reference'):
moves_dict[str(name)] = pd.DataFrame()

# for loop to fill moves_dict with the orders per day
for k, v in group.groupby(pd.Grouper(key='Date', freq='M')):
if not v.empty:
# comparing dates in inventory and movements to get the final stock
a = (inventory_df[inventory_df['REFERENCE'].values == v.tail(1)['Reference'].values])
a = (a[a['Date'].dt.month.values == v.tail(1)['Date'].dt.month.values])
a = (a[a['Date'].dt.year.values == v.tail(1)['Date'].dt.year.values])

# sum all days together and create the ones which are missing
for i, j in v.groupby(pd.Grouper(key='Date', freq='D')):
temp = j.tail(1).copy() # We need to get the last row
moves_dict[str(name)] = moves_dict[str(name)].append(j.tail(1))
if not a.empty:
# Drop last row (last day of the month) and add the one with the final stock
moves_dict[str(name)].drop(moves_dict[str(name)].index[len(moves_dict[str(name)])-1], inplace = True)
temp['final_stock'] = float(a['FINAL STOCK'])
moves_dict[str(name)] = moves_dict[str(name)].append(temp)








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    I started to work in a startup, but I'm the only data analyst, so I can't ask in my company, my problem is that in a lot of parts of my code I have to divide the dataframe, so I use the groupby to:



    for name, group in mvtos_material_df.groupby('Referencia'):
    # Work with the groups


    But I think that working with for loops on pandas is wrong, and for some easy functions I saw that the idea is to use groupby().apply() but I have also the next code which I don't how to fix it, if not putting it all in a method, but seems a wrong idea.



    moves_dict = {}

    for name, group in mvtos_material_df.groupby('Reference'):
    moves_dict[str(name)] = pd.DataFrame()

    # for loop to fill moves_dict with the orders per day
    for k, v in group.groupby(pd.Grouper(key='Date', freq='M')):
    if not v.empty:
    # comparing dates in inventory and movements to get the final stock
    a = (inventory_df[inventory_df['REFERENCE'].values == v.tail(1)['Reference'].values])
    a = (a[a['Date'].dt.month.values == v.tail(1)['Date'].dt.month.values])
    a = (a[a['Date'].dt.year.values == v.tail(1)['Date'].dt.year.values])

    # sum all days together and create the ones which are missing
    for i, j in v.groupby(pd.Grouper(key='Date', freq='D')):
    temp = j.tail(1).copy() # We need to get the last row
    moves_dict[str(name)] = moves_dict[str(name)].append(j.tail(1))
    if not a.empty:
    # Drop last row (last day of the month) and add the one with the final stock
    moves_dict[str(name)].drop(moves_dict[str(name)].index[len(moves_dict[str(name)])-1], inplace = True)
    temp['final_stock'] = float(a['FINAL STOCK'])
    moves_dict[str(name)] = moves_dict[str(name)].append(temp)








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    set92 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
    Check out our Code of Conduct.






















      up vote
      0
      down vote

      favorite









      up vote
      0
      down vote

      favorite











      I started to work in a startup, but I'm the only data analyst, so I can't ask in my company, my problem is that in a lot of parts of my code I have to divide the dataframe, so I use the groupby to:



      for name, group in mvtos_material_df.groupby('Referencia'):
      # Work with the groups


      But I think that working with for loops on pandas is wrong, and for some easy functions I saw that the idea is to use groupby().apply() but I have also the next code which I don't how to fix it, if not putting it all in a method, but seems a wrong idea.



      moves_dict = {}

      for name, group in mvtos_material_df.groupby('Reference'):
      moves_dict[str(name)] = pd.DataFrame()

      # for loop to fill moves_dict with the orders per day
      for k, v in group.groupby(pd.Grouper(key='Date', freq='M')):
      if not v.empty:
      # comparing dates in inventory and movements to get the final stock
      a = (inventory_df[inventory_df['REFERENCE'].values == v.tail(1)['Reference'].values])
      a = (a[a['Date'].dt.month.values == v.tail(1)['Date'].dt.month.values])
      a = (a[a['Date'].dt.year.values == v.tail(1)['Date'].dt.year.values])

      # sum all days together and create the ones which are missing
      for i, j in v.groupby(pd.Grouper(key='Date', freq='D')):
      temp = j.tail(1).copy() # We need to get the last row
      moves_dict[str(name)] = moves_dict[str(name)].append(j.tail(1))
      if not a.empty:
      # Drop last row (last day of the month) and add the one with the final stock
      moves_dict[str(name)].drop(moves_dict[str(name)].index[len(moves_dict[str(name)])-1], inplace = True)
      temp['final_stock'] = float(a['FINAL STOCK'])
      moves_dict[str(name)] = moves_dict[str(name)].append(temp)








      share







      New contributor




      set92 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
      Check out our Code of Conduct.











      I started to work in a startup, but I'm the only data analyst, so I can't ask in my company, my problem is that in a lot of parts of my code I have to divide the dataframe, so I use the groupby to:



      for name, group in mvtos_material_df.groupby('Referencia'):
      # Work with the groups


      But I think that working with for loops on pandas is wrong, and for some easy functions I saw that the idea is to use groupby().apply() but I have also the next code which I don't how to fix it, if not putting it all in a method, but seems a wrong idea.



      moves_dict = {}

      for name, group in mvtos_material_df.groupby('Reference'):
      moves_dict[str(name)] = pd.DataFrame()

      # for loop to fill moves_dict with the orders per day
      for k, v in group.groupby(pd.Grouper(key='Date', freq='M')):
      if not v.empty:
      # comparing dates in inventory and movements to get the final stock
      a = (inventory_df[inventory_df['REFERENCE'].values == v.tail(1)['Reference'].values])
      a = (a[a['Date'].dt.month.values == v.tail(1)['Date'].dt.month.values])
      a = (a[a['Date'].dt.year.values == v.tail(1)['Date'].dt.year.values])

      # sum all days together and create the ones which are missing
      for i, j in v.groupby(pd.Grouper(key='Date', freq='D')):
      temp = j.tail(1).copy() # We need to get the last row
      moves_dict[str(name)] = moves_dict[str(name)].append(j.tail(1))
      if not a.empty:
      # Drop last row (last day of the month) and add the one with the final stock
      moves_dict[str(name)].drop(moves_dict[str(name)].index[len(moves_dict[str(name)])-1], inplace = True)
      temp['final_stock'] = float(a['FINAL STOCK'])
      moves_dict[str(name)] = moves_dict[str(name)].append(temp)






      python performance pandas





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      set92 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
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      set92 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
      Check out our Code of Conduct.






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      Check out our Code of Conduct.



























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