Break Dataframe into Smaller Dataframe'S and Save Them

Break dataframe into smaller dataframe's and save them

You can use the split function and cut function to perform the operation:

x = data.frame(num = 1:26, let = letters, LET = LETTERS)

answer<-split(x, cut(x$num, breaks=c(0, 5, 10, 15, 20, 25, 30)))

you can then pass this list to lapply for further processing.

Split a dataframe into smaller dataframes in R using dplyr

We may use gl to create the grouping column in group_split

library(dplyr)
df1 %>%
      group_split(grp = as.integer(gl(n(), 59, n())), .keep = FALSE)

Split a large pandas dataframe

Use np.array_split:

Docstring:
Split an array into multiple sub-arrays.

Please refer to the ``split`` documentation.  The only difference
between these functions is that ``array_split`` allows
`indices_or_sections` to be an integer that does *not* equally
divide the axis.

In [1]: import pandas as pd

In [2]: df = pd.DataFrame({'A' : ['foo', 'bar', 'foo', 'bar',
   ...:                           'foo', 'bar', 'foo', 'foo'],
   ...:                    'B' : ['one', 'one', 'two', 'three',
   ...:                           'two', 'two', 'one', 'three'],
   ...:                    'C' : randn(8), 'D' : randn(8)})

In [3]: print df
     A      B         C         D
0  foo    one -0.174067 -0.608579
1  bar    one -0.860386 -1.210518
2  foo    two  0.614102  1.689837
3  bar  three -0.284792 -1.071160
4  foo    two  0.843610  0.803712
5  bar    two -1.514722  0.870861
6  foo    one  0.131529 -0.968151
7  foo  three -1.002946 -0.257468

In [4]: import numpy as np
In [5]: np.array_split(df, 3)
Out[5]: 
[     A    B         C         D
0  foo  one -0.174067 -0.608579
1  bar  one -0.860386 -1.210518
2  foo  two  0.614102  1.689837,
      A      B         C         D
3  bar  three -0.284792 -1.071160
4  foo    two  0.843610  0.803712
5  bar    two -1.514722  0.870861,
      A      B         C         D
6  foo    one  0.131529 -0.968151
7  foo  three -1.002946 -0.257468]

Split dataframe into smaller dataframe by column Names

Assume this is your dataframe:

 Name  price
0   aal      1
1   aal      2
2   aal      3
3   aal      4
4   aal      5
5   aal      6
6   bll      7
7   bll      8
8   bll      9
9   bll      8
10  dll      7
11  dll     56
12  dll      4
13  dll      3
14  dll      3
15  dll      5

Then do the following:

for Name, df in df.groupby('Name'):
    df.to_csv("Price_{}".format(Name)+".csv", sep=";")

That'll save all sub-dataframes as csv.
To view what the code does:

for Name, df in df.groupby('Name'):
    print(df)

returns:

Name  price
0  aal      1
1  aal      2
2  aal      3
3  aal      4
4  aal      5
5  aal      6
  Name  price
6  bll      7
7  bll      8
8  bll      9
9  bll      8
   Name  price
10  dll      7
11  dll     56
12  dll      4
13  dll      3
14  dll      3
15  dll      5

If you need to reset the index in every df, do this:

for Name, df in df.groupby('Name'):
    gf = df.reset_index()
    print(gf)

which gives:

index Name  price
0      0  aal      1
1      1  aal      2
2      2  aal      3
3      3  aal      4
4      4  aal      5
5      5  aal      6
   index Name  price
0      6  bll      7
1      7  bll      8
2      8  bll      9
3      9  bll      8
   index Name  price
0     10  dll      7
1     11  dll     56
2     12  dll      4
3     13  dll      3
4     14  dll      3
5     15  dll      5

Split dataframe into 3 equally sized new dataframes - Pandas

Try using numpy.array_split:

import numpy as np
df1, df2, df3 = np.array_split(df_seen, 3)

To save each DataFrame to a separate file, you could do:

for i, df in enumerate(np.array_split(df_seen, 3)):
    df.to_csv(f"data{i+1}.csv", index=False)

Split large Dataframe into smaller equal dataframes

I don't know from your description if you are aware that np.array_split outputs n objects. If it's only a few objects you could manually assign them, for example:

df1, df2, df3 = np.array_split(df, 3)

This would assign every subarray to these variables in order.
Otherwise you could assign the series of subarrays to a single variable;

split_df = np.array_split(df, 3)
len(split_df)
# 3

then loop over this one variable and do your analysis per subarray. I would personally choose the latter.

for object in split_df:
    print(type(object))

This prints <class 'pandas.core.frame.DataFrame'> three times.

Splitting dataframe into multiple dataframes

Firstly your approach is inefficient because the appending to the list on a row by basis will be slow as it has to periodically grow the list when there is insufficient space for the new entry, list comprehensions are better in this respect as the size is determined up front and allocated once.

However, I think fundamentally your approach is a little wasteful as you have a dataframe already so why create a new one for each of these users?

I would sort the dataframe by column 'name', set the index to be this and if required not drop the column.

Then generate a list of all the unique entries and then you can perform a lookup using these entries and crucially if you only querying the data, use the selection criteria to return a view on the dataframe without incurring a costly data copy.

Use pandas.DataFrame.sort_values and pandas.DataFrame.set_index:

# sort the dataframe
df.sort_values(by='name', axis=1, inplace=True)

# set the index to be this and don't drop
df.set_index(keys=['name'], drop=False,inplace=True)

# get a list of names
names=df['name'].unique().tolist()

# now we can perform a lookup on a 'view' of the dataframe
joe = df.loc[df.name=='joe']

# now you can query all 'joes'

Divide a large dataframe into smaller sub dataframes in order

Following my comment. Here is an example, note it's probably not the best approach..:

import numpy as np
dfs = np.array_split(df2, 5)
for index, df in enumerate(dfs):
    globals()['df%s' % index] = pd.DataFrame(df)

df3

How to randomly split a DataFrame into several smaller DataFrames?

Use np.array_split

shuffled = df.sample(frac=1)
result = np.array_split(shuffled, 5)

df.sample(frac=1) shuffle the rows of df. Then use np.array_split split it into parts that have equal size.

It gives you:

for part in result:
    print(part,'\n')

    movie_id  1  2  4  5  6  7  8  9  10  11  12  borda
5          6  5  0  0  0  0  0  0  5   0   0   0     10
4          5  3  0  0  0  0  0  0  0   0   0   0      3
7          8  1  0  0  0  4  5  0  0   0   4   0     14
16        17  3  0  0  4  0  0  0  0   0   0   0      7
22        23  4  0  0  0  4  3  0  0   5   0   0     16 

    movie_id  1  2  4  5  6  7  8  9  10  11  12  borda
13        14  5  4  0  0  5  0  0  0   0   0   0     14
14        15  5  0  0  0  3  0  0  0   0   5   5     18
21        22  4  0  0  0  3  5  5  0   5   4   0     26
1          2  3  0  0  3  0  0  0  0   0   0   0      6
20        21  1  0  0  3  3  0  0  0   0   0   0      7 

    movie_id  1  2  4  5  6  7  8  9  10  11  12  borda
10        11  2  0  4  0  0  3  3  0   4   2   0     18
9         10  3  2  0  0  0  4  0  0   0   0   0      9
11        12  5  0  0  0  4  5  0  0   5   2   0     21
8          9  5  0  0  0  4  5  0  0   4   5   0     23
12        13  5  4  0  0  2  0  0  0   3   0   0     14 

    movie_id  1  2  4  5  6  7  8  9  10  11  12  borda
18        19  5  3  0  0  4  0  0  0   0   0   0     12
3          4  3  0  0  0  0  5  0  0   4   0   5     17
0          1  5  4  0  4  4  0  0  0   4   0   0     21
23        24  3  0  0  4  0  0  0  0   0   3   0     10
6          7  4  0  0  0  2  5  3  4   4   0   0     22 

    movie_id  1  2  4  5  6  7  8  9  10  11  12  borda
17        18  4  0  0  0  0  0  0  0   0   0   0      4
2          3  4  0  0  0  0  0  0  0   0   0   0      4
15        16  5  0  0  0  0  0  0  0   4   0   0      9
19        20  4  0  0  0  0  0  0  0   0   0   0      4

Break Dataframe into Smaller Dataframe'S and Save Them