How to extract rows, from datatframe, based on column value, to multiple CSV files?
I have following dataframe:
data = {'participant_id': [1, 100, 125, 125, 1, 100],
'test_day':['Day_1', 'Day_1', 'Day_12', 'Day_14', 'Day_4', 'Day_4'],
'favorite_color': ['blue', 'red', 'yellow', 'green', 'yellow', 'green'],
'grade': [88, 92, 95, 70, 80, 30]}
df = pd.DataFrame(data, columns = ['participant_id', 'test_day', 'favorite_color', 'grade'])
It has 10000 rows and contains data for 400 test participants labelled with unique and completely random ID’s stored in 'participant_id' column. My task is to create dataframes for individuals (per ‘participant_id’) and then save them to the separate csv files (400 in total).
I’ve been trying to figure out how to do it for a couple of days now but with no luck.
Can you please help me?
I am still learning how to program and trying to apply knowledge from data science course. I am using Pandas and normally I access data about individual participant with df.loc, I have also created a list of all of the participant_id’s but I don’t know how to combine both to achieve the desired result automatically.
python pandas dataframe pandas-groupby
edited Nov 25 '18 at 20:49
jpp
99.4k2161110
asked Nov 25 '18 at 0:56
MoonlitMoonlit
207
add a comment |
I have following dataframe:
data = {'participant_id': [1, 100, 125, 125, 1, 100],
'test_day':['Day_1', 'Day_1', 'Day_12', 'Day_14', 'Day_4', 'Day_4'],
'favorite_color': ['blue', 'red', 'yellow', 'green', 'yellow', 'green'],
'grade': [88, 92, 95, 70, 80, 30]}
df = pd.DataFrame(data, columns = ['participant_id', 'test_day', 'favorite_color', 'grade'])
It has 10000 rows and contains data for 400 test participants labelled with unique and completely random ID’s stored in 'participant_id' column. My task is to create dataframes for individuals (per ‘participant_id’) and then save them to the separate csv files (400 in total).
I’ve been trying to figure out how to do it for a couple of days now but with no luck.
Can you please help me?
I am still learning how to program and trying to apply knowledge from data science course. I am using Pandas and normally I access data about individual participant with df.loc, I have also created a list of all of the participant_id’s but I don’t know how to combine both to achieve the desired result automatically.
python pandas dataframe pandas-groupby
edited Nov 25 '18 at 20:49
jpp
99.4k2161110
asked Nov 25 '18 at 0:56
MoonlitMoonlit
207
add a comment |
I have following dataframe:
data = {'participant_id': [1, 100, 125, 125, 1, 100],
'test_day':['Day_1', 'Day_1', 'Day_12', 'Day_14', 'Day_4', 'Day_4'],
'favorite_color': ['blue', 'red', 'yellow', 'green', 'yellow', 'green'],
'grade': [88, 92, 95, 70, 80, 30]}
df = pd.DataFrame(data, columns = ['participant_id', 'test_day', 'favorite_color', 'grade'])
It has 10000 rows and contains data for 400 test participants labelled with unique and completely random ID’s stored in 'participant_id' column. My task is to create dataframes for individuals (per ‘participant_id’) and then save them to the separate csv files (400 in total).
I’ve been trying to figure out how to do it for a couple of days now but with no luck.
Can you please help me?
I am still learning how to program and trying to apply knowledge from data science course. I am using Pandas and normally I access data about individual participant with df.loc, I have also created a list of all of the participant_id’s but I don’t know how to combine both to achieve the desired result automatically.
python pandas dataframe pandas-groupby
edited Nov 25 '18 at 20:49
jpp
99.4k2161110
asked Nov 25 '18 at 0:56
MoonlitMoonlit
207
I have following dataframe:
data = {'participant_id': [1, 100, 125, 125, 1, 100],
'test_day':['Day_1', 'Day_1', 'Day_12', 'Day_14', 'Day_4', 'Day_4'],
'favorite_color': ['blue', 'red', 'yellow', 'green', 'yellow', 'green'],
'grade': [88, 92, 95, 70, 80, 30]}
df = pd.DataFrame(data, columns = ['participant_id', 'test_day', 'favorite_color', 'grade'])
It has 10000 rows and contains data for 400 test participants labelled with unique and completely random ID’s stored in 'participant_id' column. My task is to create dataframes for individuals (per ‘participant_id’) and then save them to the separate csv files (400 in total).
I’ve been trying to figure out how to do it for a couple of days now but with no luck.
Can you please help me?
I am still learning how to program and trying to apply knowledge from data science course. I am using Pandas and normally I access data about individual participant with df.loc, I have also created a list of all of the participant_id’s but I don’t know how to combine both to achieve the desired result automatically.
python pandas dataframe pandas-groupby
python pandas dataframe pandas-groupby
edited Nov 25 '18 at 20:49
jpp
99.4k2161110
asked Nov 25 '18 at 0:56
MoonlitMoonlit
207
edited Nov 25 '18 at 20:49
jpp
99.4k2161110
asked Nov 25 '18 at 0:56
MoonlitMoonlit
207
edited Nov 25 '18 at 20:49
jpp
99.4k2161110
edited Nov 25 '18 at 20:49
jpp
99.4k2161110
edited Nov 25 '18 at 20:49
jpp
99.4k2161110
99.4k2161110
asked Nov 25 '18 at 0:56
MoonlitMoonlit
207
asked Nov 25 '18 at 0:56
MoonlitMoonlit
207
asked Nov 25 '18 at 0:56
MoonlitMoonlit
207
207
add a comment |
add a comment |
2 Answers
2
active
oldest
votes
groupby + to_csv
You can group by a particular field and iterate:
for part_id, df_id in df.groupby('participant_id'):
df_id.to_csv(f'{part_id}.csv')
answered Nov 25 '18 at 1:00
jppjpp
99.4k2161110
Thank you @jpp for this elegant and simple answer. Files are extracted =D!
– Moonlit
Nov 25 '18 at 18:33
I have one more question:). If I would like to use an automatic prefix and instead of IDs 10011, 13652 have A10011 and A13652 what I suppose to do? Cheers!
– Moonlit
Dec 3 '18 at 1:44
1
How aboutdf_id.to_csv(f'A{part_id}.csv')
– jpp
Dec 3 '18 at 9:22
add a comment |
Solution by @jpp is great. My adaptation based on your solution is
import pandas as pd
import numpy as np
data = {'participant_id': [1, 100, 125, 125, 1, 100],
'test_day':['Day_1', 'Day_1', 'Day_12', 'Day_14', 'Day_4', 'Day_4'],
'favorite_color': ['blue', 'red', 'yellow', 'green', 'yellow', 'green'],
'grade': [88, 92, 95, 70, 80, 30]
}
col = list(data.keys())
df = pd.DataFrame(data, columns = col)
for part_id, df_id in df.groupby('participant_id'):
df_id.to_csv(f'{part_id}.csv',index=False)
answered Nov 25 '18 at 1:53
yoonghmyoonghm
1,086918
add a comment |
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2 Answers
2
active
oldest
votes
2 Answers
2
active
oldest
votes
active
oldest
votes
active
oldest
votes
groupby + to_csv
You can group by a particular field and iterate:
for part_id, df_id in df.groupby('participant_id'):
df_id.to_csv(f'{part_id}.csv')
answered Nov 25 '18 at 1:00
jppjpp
99.4k2161110
Thank you @jpp for this elegant and simple answer. Files are extracted =D!
– Moonlit
Nov 25 '18 at 18:33
I have one more question:). If I would like to use an automatic prefix and instead of IDs 10011, 13652 have A10011 and A13652 what I suppose to do? Cheers!
– Moonlit
Dec 3 '18 at 1:44
1
How aboutdf_id.to_csv(f'A{part_id}.csv')
– jpp
Dec 3 '18 at 9:22
add a comment |
groupby + to_csv
You can group by a particular field and iterate:
for part_id, df_id in df.groupby('participant_id'):
df_id.to_csv(f'{part_id}.csv')
answered Nov 25 '18 at 1:00
jppjpp
99.4k2161110
Thank you @jpp for this elegant and simple answer. Files are extracted =D!
– Moonlit
Nov 25 '18 at 18:33
I have one more question:). If I would like to use an automatic prefix and instead of IDs 10011, 13652 have A10011 and A13652 what I suppose to do? Cheers!
– Moonlit
Dec 3 '18 at 1:44
1
How aboutdf_id.to_csv(f'A{part_id}.csv')
– jpp
Dec 3 '18 at 9:22
add a comment |
groupby + to_csv
You can group by a particular field and iterate:
for part_id, df_id in df.groupby('participant_id'):
df_id.to_csv(f'{part_id}.csv')
answered Nov 25 '18 at 1:00
jppjpp
99.4k2161110
groupby + to_csv
You can group by a particular field and iterate:
for part_id, df_id in df.groupby('participant_id'):
df_id.to_csv(f'{part_id}.csv')
answered Nov 25 '18 at 1:00
jppjpp
99.4k2161110
answered Nov 25 '18 at 1:00
jppjpp
99.4k2161110
answered Nov 25 '18 at 1:00
jppjpp
99.4k2161110
answered Nov 25 '18 at 1:00
jppjpp
99.4k2161110
99.4k2161110
Thank you @jpp for this elegant and simple answer. Files are extracted =D!
– Moonlit
Nov 25 '18 at 18:33
I have one more question:). If I would like to use an automatic prefix and instead of IDs 10011, 13652 have A10011 and A13652 what I suppose to do? Cheers!
– Moonlit
Dec 3 '18 at 1:44
1
How aboutdf_id.to_csv(f'A{part_id}.csv')
– jpp
Dec 3 '18 at 9:22
add a comment |
Thank you @jpp for this elegant and simple answer. Files are extracted =D!
– Moonlit
Nov 25 '18 at 18:33
I have one more question:). If I would like to use an automatic prefix and instead of IDs 10011, 13652 have A10011 and A13652 what I suppose to do? Cheers!
– Moonlit
Dec 3 '18 at 1:44
1
How aboutdf_id.to_csv(f'A{part_id}.csv')
– jpp
Dec 3 '18 at 9:22
Thank you @jpp for this elegant and simple answer. Files are extracted =D!
– Moonlit
Nov 25 '18 at 18:33
Thank you @jpp for this elegant and simple answer. Files are extracted =D!
– Moonlit
Nov 25 '18 at 18:33
I have one more question:). If I would like to use an automatic prefix and instead of IDs 10011, 13652 have A10011 and A13652 what I suppose to do? Cheers!
– Moonlit
Dec 3 '18 at 1:44
I have one more question:). If I would like to use an automatic prefix and instead of IDs 10011, 13652 have A10011 and A13652 what I suppose to do? Cheers!
– Moonlit
Dec 3 '18 at 1:44
1
1
How about
df_id.to_csv(f'A{part_id}.csv')– jpp
Dec 3 '18 at 9:22
How about
df_id.to_csv(f'A{part_id}.csv')– jpp
Dec 3 '18 at 9:22
add a comment |
Solution by @jpp is great. My adaptation based on your solution is
import pandas as pd
import numpy as np
data = {'participant_id': [1, 100, 125, 125, 1, 100],
'test_day':['Day_1', 'Day_1', 'Day_12', 'Day_14', 'Day_4', 'Day_4'],
'favorite_color': ['blue', 'red', 'yellow', 'green', 'yellow', 'green'],
'grade': [88, 92, 95, 70, 80, 30]
}
col = list(data.keys())
df = pd.DataFrame(data, columns = col)
for part_id, df_id in df.groupby('participant_id'):
df_id.to_csv(f'{part_id}.csv',index=False)
answered Nov 25 '18 at 1:53
yoonghmyoonghm
1,086918
add a comment |
Solution by @jpp is great. My adaptation based on your solution is
import pandas as pd
import numpy as np
data = {'participant_id': [1, 100, 125, 125, 1, 100],
'test_day':['Day_1', 'Day_1', 'Day_12', 'Day_14', 'Day_4', 'Day_4'],
'favorite_color': ['blue', 'red', 'yellow', 'green', 'yellow', 'green'],
'grade': [88, 92, 95, 70, 80, 30]
}
col = list(data.keys())
df = pd.DataFrame(data, columns = col)
for part_id, df_id in df.groupby('participant_id'):
df_id.to_csv(f'{part_id}.csv',index=False)
answered Nov 25 '18 at 1:53
yoonghmyoonghm
1,086918
add a comment |
Solution by @jpp is great. My adaptation based on your solution is
import pandas as pd
import numpy as np
data = {'participant_id': [1, 100, 125, 125, 1, 100],
'test_day':['Day_1', 'Day_1', 'Day_12', 'Day_14', 'Day_4', 'Day_4'],
'favorite_color': ['blue', 'red', 'yellow', 'green', 'yellow', 'green'],
'grade': [88, 92, 95, 70, 80, 30]
}
col = list(data.keys())
df = pd.DataFrame(data, columns = col)
for part_id, df_id in df.groupby('participant_id'):
df_id.to_csv(f'{part_id}.csv',index=False)
answered Nov 25 '18 at 1:53
yoonghmyoonghm
1,086918
Solution by @jpp is great. My adaptation based on your solution is
import pandas as pd
import numpy as np
data = {'participant_id': [1, 100, 125, 125, 1, 100],
'test_day':['Day_1', 'Day_1', 'Day_12', 'Day_14', 'Day_4', 'Day_4'],
'favorite_color': ['blue', 'red', 'yellow', 'green', 'yellow', 'green'],
'grade': [88, 92, 95, 70, 80, 30]
}
col = list(data.keys())
df = pd.DataFrame(data, columns = col)
for part_id, df_id in df.groupby('participant_id'):
df_id.to_csv(f'{part_id}.csv',index=False)
answered Nov 25 '18 at 1:53
yoonghmyoonghm
1,086918
answered Nov 25 '18 at 1:53
yoonghmyoonghm
1,086918
answered Nov 25 '18 at 1:53
yoonghmyoonghm
1,086918
answered Nov 25 '18 at 1:53
yoonghmyoonghm
1,086918
1,086918
add a comment |
add a comment |
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