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)
python performance pandas
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up vote
0
down vote
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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)
python performance pandas
asked 3 mins ago
set92
1
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.
add a comment |
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)
python performance pandas
asked 3 mins ago
set92
1
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
python performance pandas
asked 3 mins ago
set92
1
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.
asked 3 mins ago
set92
1
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.
asked 3 mins ago
set92
1
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.
asked 3 mins ago
set92
1
asked 3 mins ago
set92
1
1
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.
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.
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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