splitting a dataframe into chunks and naming each new chunk into a dataframe
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0
down vote
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is there a good code to split dataframes into chunks and automatically name each chunk into its own dataframe?
for example, dfmaster has 1000 records. split by 200 and create df1, df2,….df5
any guidance would be much appreciated.
I've looked on other boards and there is no guidance for a function that can automatically create new dataframes.
python loops dataframe split chunks
edited Nov 15 at 7:05
Torxed
13.1k105486
asked Nov 15 at 7:03
pynewbee
124112
add a comment |
up vote
0
down vote
favorite
is there a good code to split dataframes into chunks and automatically name each chunk into its own dataframe?
for example, dfmaster has 1000 records. split by 200 and create df1, df2,….df5
any guidance would be much appreciated.
I've looked on other boards and there is no guidance for a function that can automatically create new dataframes.
python loops dataframe split chunks
edited Nov 15 at 7:05
Torxed
13.1k105486
asked Nov 15 at 7:03
pynewbee
124112
If you're reading your data with pd.read_csv or anything similar, you can use the chunksize-parameter: pandas.pydata.org/pandas-docs/version/0.23/generated/…. You'll make a simple for chunk in pd.read_csv(chunksize=200), and so on.
– RoyM
Nov 15 at 7:08
add a comment |
up vote
0
down vote
favorite
up vote
0
down vote
favorite
is there a good code to split dataframes into chunks and automatically name each chunk into its own dataframe?
for example, dfmaster has 1000 records. split by 200 and create df1, df2,….df5
any guidance would be much appreciated.
I've looked on other boards and there is no guidance for a function that can automatically create new dataframes.
python loops dataframe split chunks
edited Nov 15 at 7:05
Torxed
13.1k105486
asked Nov 15 at 7:03
pynewbee
124112
is there a good code to split dataframes into chunks and automatically name each chunk into its own dataframe?
for example, dfmaster has 1000 records. split by 200 and create df1, df2,….df5
any guidance would be much appreciated.
I've looked on other boards and there is no guidance for a function that can automatically create new dataframes.
python loops dataframe split chunks
python loops dataframe split chunks
edited Nov 15 at 7:05
Torxed
13.1k105486
asked Nov 15 at 7:03
pynewbee
124112
edited Nov 15 at 7:05
Torxed
13.1k105486
asked Nov 15 at 7:03
pynewbee
124112
edited Nov 15 at 7:05
Torxed
13.1k105486
edited Nov 15 at 7:05
Torxed
13.1k105486
edited Nov 15 at 7:05
Torxed
13.1k105486
13.1k105486
asked Nov 15 at 7:03
pynewbee
124112
asked Nov 15 at 7:03
pynewbee
124112
asked Nov 15 at 7:03
pynewbee
124112
124112
If you're reading your data with pd.read_csv or anything similar, you can use the chunksize-parameter: pandas.pydata.org/pandas-docs/version/0.23/generated/…. You'll make a simple for chunk in pd.read_csv(chunksize=200), and so on.
– RoyM
Nov 15 at 7:08
add a comment |
If you're reading your data with pd.read_csv or anything similar, you can use the chunksize-parameter: pandas.pydata.org/pandas-docs/version/0.23/generated/…. You'll make a simple for chunk in pd.read_csv(chunksize=200), and so on.
– RoyM
Nov 15 at 7:08
If you're reading your data with pd.read_csv or anything similar, you can use the chunksize-parameter: pandas.pydata.org/pandas-docs/version/0.23/generated/…. You'll make a simple for chunk in pd.read_csv(chunksize=200), and so on.
– RoyM
Nov 15 at 7:08
If you're reading your data with pd.read_csv or anything similar, you can use the chunksize-parameter: pandas.pydata.org/pandas-docs/version/0.23/generated/…. You'll make a simple for chunk in pd.read_csv(chunksize=200), and so on.
– RoyM
Nov 15 at 7:08
add a comment |
2 Answers
2
active
oldest
votes
up vote
1
down vote
Use numpy for splitting:
See example below:
In [2095]: df
Out[2095]:
0 1 2 3 4 5 6 7 8 9 10
0 0.25 0.00 0.00 0.0 0.00 0.0 0.94 0.00 0.00 0.63 0.00
1 0.51 0.51 NaN NaN NaN NaN NaN NaN NaN NaN NaN
2 0.54 0.54 0.00 0.0 0.63 0.0 0.51 0.54 0.51 1.00 0.51
3 0.81 0.05 0.13 0.7 0.02 NaN NaN NaN NaN NaN NaN
In [2096]: np.split(df, 2)
Out[2096]:
[ 0 1 2 3 4 5 6 7 8 9 10
0 0.25 0.00 0.0 0.0 0.0 0.0 0.94 0.0 0.0 0.63 0.0
1 0.51 0.51 NaN NaN NaN NaN NaN NaN NaN NaN NaN,
0 1 2 3 4 5 6 7 8 9 10
2 0.54 0.54 0.00 0.0 0.63 0.0 0.51 0.54 0.51 1.0 0.51
3 0.81 0.05 0.13 0.7 0.02 NaN NaN NaN NaN NaN NaN]
df gets split into 2 dataframes having 2 rows each.
You can do np.split(df, 500)
answered Nov 15 at 7:15
Mayank Porwal
3,8601621
add a comment |
up vote
0
down vote
I find these ideas helpful:
solution via list:
https://stackoverflow.com/a/49563326/10396469
solution using numpy.split:
https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.split.html
just use df = df.values first to convert from dataframe to numpy.array.
answered Nov 15 at 7:12
Ruslan S.
63
add a comment |
2 Answers
2
active
oldest
votes
2 Answers
2
active
oldest
votes
active
oldest
votes
active
oldest
votes
up vote
1
down vote
Use numpy for splitting:
See example below:
In [2095]: df
Out[2095]:
0 1 2 3 4 5 6 7 8 9 10
0 0.25 0.00 0.00 0.0 0.00 0.0 0.94 0.00 0.00 0.63 0.00
1 0.51 0.51 NaN NaN NaN NaN NaN NaN NaN NaN NaN
2 0.54 0.54 0.00 0.0 0.63 0.0 0.51 0.54 0.51 1.00 0.51
3 0.81 0.05 0.13 0.7 0.02 NaN NaN NaN NaN NaN NaN
In [2096]: np.split(df, 2)
Out[2096]:
[ 0 1 2 3 4 5 6 7 8 9 10
0 0.25 0.00 0.0 0.0 0.0 0.0 0.94 0.0 0.0 0.63 0.0
1 0.51 0.51 NaN NaN NaN NaN NaN NaN NaN NaN NaN,
0 1 2 3 4 5 6 7 8 9 10
2 0.54 0.54 0.00 0.0 0.63 0.0 0.51 0.54 0.51 1.0 0.51
3 0.81 0.05 0.13 0.7 0.02 NaN NaN NaN NaN NaN NaN]
df gets split into 2 dataframes having 2 rows each.
You can do np.split(df, 500)
answered Nov 15 at 7:15
Mayank Porwal
3,8601621
add a comment |
up vote
1
down vote
Use numpy for splitting:
See example below:
In [2095]: df
Out[2095]:
0 1 2 3 4 5 6 7 8 9 10
0 0.25 0.00 0.00 0.0 0.00 0.0 0.94 0.00 0.00 0.63 0.00
1 0.51 0.51 NaN NaN NaN NaN NaN NaN NaN NaN NaN
2 0.54 0.54 0.00 0.0 0.63 0.0 0.51 0.54 0.51 1.00 0.51
3 0.81 0.05 0.13 0.7 0.02 NaN NaN NaN NaN NaN NaN
In [2096]: np.split(df, 2)
Out[2096]:
[ 0 1 2 3 4 5 6 7 8 9 10
0 0.25 0.00 0.0 0.0 0.0 0.0 0.94 0.0 0.0 0.63 0.0
1 0.51 0.51 NaN NaN NaN NaN NaN NaN NaN NaN NaN,
0 1 2 3 4 5 6 7 8 9 10
2 0.54 0.54 0.00 0.0 0.63 0.0 0.51 0.54 0.51 1.0 0.51
3 0.81 0.05 0.13 0.7 0.02 NaN NaN NaN NaN NaN NaN]
df gets split into 2 dataframes having 2 rows each.
You can do np.split(df, 500)
answered Nov 15 at 7:15
Mayank Porwal
3,8601621
add a comment |
up vote
1
down vote
up vote
1
down vote
Use numpy for splitting:
See example below:
In [2095]: df
Out[2095]:
0 1 2 3 4 5 6 7 8 9 10
0 0.25 0.00 0.00 0.0 0.00 0.0 0.94 0.00 0.00 0.63 0.00
1 0.51 0.51 NaN NaN NaN NaN NaN NaN NaN NaN NaN
2 0.54 0.54 0.00 0.0 0.63 0.0 0.51 0.54 0.51 1.00 0.51
3 0.81 0.05 0.13 0.7 0.02 NaN NaN NaN NaN NaN NaN
In [2096]: np.split(df, 2)
Out[2096]:
[ 0 1 2 3 4 5 6 7 8 9 10
0 0.25 0.00 0.0 0.0 0.0 0.0 0.94 0.0 0.0 0.63 0.0
1 0.51 0.51 NaN NaN NaN NaN NaN NaN NaN NaN NaN,
0 1 2 3 4 5 6 7 8 9 10
2 0.54 0.54 0.00 0.0 0.63 0.0 0.51 0.54 0.51 1.0 0.51
3 0.81 0.05 0.13 0.7 0.02 NaN NaN NaN NaN NaN NaN]
df gets split into 2 dataframes having 2 rows each.
You can do np.split(df, 500)
answered Nov 15 at 7:15
Mayank Porwal
3,8601621
Use numpy for splitting:
See example below:
In [2095]: df
Out[2095]:
0 1 2 3 4 5 6 7 8 9 10
0 0.25 0.00 0.00 0.0 0.00 0.0 0.94 0.00 0.00 0.63 0.00
1 0.51 0.51 NaN NaN NaN NaN NaN NaN NaN NaN NaN
2 0.54 0.54 0.00 0.0 0.63 0.0 0.51 0.54 0.51 1.00 0.51
3 0.81 0.05 0.13 0.7 0.02 NaN NaN NaN NaN NaN NaN
In [2096]: np.split(df, 2)
Out[2096]:
[ 0 1 2 3 4 5 6 7 8 9 10
0 0.25 0.00 0.0 0.0 0.0 0.0 0.94 0.0 0.0 0.63 0.0
1 0.51 0.51 NaN NaN NaN NaN NaN NaN NaN NaN NaN,
0 1 2 3 4 5 6 7 8 9 10
2 0.54 0.54 0.00 0.0 0.63 0.0 0.51 0.54 0.51 1.0 0.51
3 0.81 0.05 0.13 0.7 0.02 NaN NaN NaN NaN NaN NaN]
df gets split into 2 dataframes having 2 rows each.
You can do np.split(df, 500)
answered Nov 15 at 7:15
Mayank Porwal
3,8601621
answered Nov 15 at 7:15
Mayank Porwal
3,8601621
answered Nov 15 at 7:15
Mayank Porwal
3,8601621
answered Nov 15 at 7:15
Mayank Porwal
3,8601621
3,8601621
add a comment |
add a comment |
up vote
0
down vote
I find these ideas helpful:
solution via list:
https://stackoverflow.com/a/49563326/10396469
solution using numpy.split:
https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.split.html
just use df = df.values first to convert from dataframe to numpy.array.
answered Nov 15 at 7:12
Ruslan S.
63
add a comment |
up vote
0
down vote
I find these ideas helpful:
solution via list:
https://stackoverflow.com/a/49563326/10396469
solution using numpy.split:
https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.split.html
just use df = df.values first to convert from dataframe to numpy.array.
answered Nov 15 at 7:12
Ruslan S.
63
add a comment |
up vote
0
down vote
up vote
0
down vote
I find these ideas helpful:
solution via list:
https://stackoverflow.com/a/49563326/10396469
solution using numpy.split:
https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.split.html
just use df = df.values first to convert from dataframe to numpy.array.
answered Nov 15 at 7:12
Ruslan S.
63
I find these ideas helpful:
solution via list:
https://stackoverflow.com/a/49563326/10396469
solution using numpy.split:
https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.split.html
just use df = df.values first to convert from dataframe to numpy.array.
answered Nov 15 at 7:12
Ruslan S.
63
answered Nov 15 at 7:12
Ruslan S.
63
answered Nov 15 at 7:12
Ruslan S.
63
answered Nov 15 at 7:12
Ruslan S.
63
63
add a comment |
add a comment |
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If you're reading your data with pd.read_csv or anything similar, you can use the chunksize-parameter: pandas.pydata.org/pandas-docs/version/0.23/generated/…. You'll make a simple for chunk in pd.read_csv(chunksize=200), and so on.
– RoyM
Nov 15 at 7:08