Converting list of 2D Panda's DataFrame to 3D DataFrame
Question
I am trying to create a Pandas DataFrame that holds label values to a 2D DataFrame. This is what I have done so far:
I am reading csv files using pd.read_csv and appending them to list, for the purpose of this question let's consider the following code:
import numpy as np
import pandas as pd
raw_sample = []
labels = [1,1,1,2,2,2]
samples = np.random.randn(6, 5, 4)
for contents in range(samples.shape[0]):
raw_sample.append(pd.DataFrame(samples[contents]))
Then, I added raw_sample to df=d.DataFrame(raw_sample) . Then I added the labels to df by doing the following:
df = df.set_index([df.index, labels])
df.index = df.index.set_names('index', level=0)
df.index = df.index.set_names('labels', level=1)
I tried printing this and I got
0
index labels
0 1 0 1 2 3
0 0...
1 1 0 1 2 3
0 0...
2 1 0 1 2 3
0 1...
3 2 0 1 2 3
0 -0...
4 2 0 1 2 3
0 0...
5 2 0 1 2 3
0 -0...
I have also tried printing df[0] , I still got the same thing.
I wanted to know if it is in the form of
index labels 0
0 1 1 2 3 4 5 6 7
3 5 6 7 9 5 4
3 4 5 6 7 8 9
1 1 4 3 2 4 5 6 7
3 5 6 7 4 5 6
2 3 4 3 4 5 3
...
I know that a DataFrame cannot take 2D array, the other thing was to use pd.Panel , for this I converted all the contents of raw_sample to numpy array and then converted raw_sample itself to numpy array and did the following:
p1 = pd.Panel(samples, items=map(str, labels))
but when I print this, I get
<class 'pandas.core.panel.Panel'>
Dimensions: 6 (items) x 5 (major_axis) x 4 (minor_axis)
Items axis: 1 to 2
Major_axis axis: 0 to 4
Minor_axis axis: 0 to 3
Looking at the Items , it looks like all the common values are grouped together.
I am not sure what to do at this point. Help!!
Update
Inputs:
labels = [1,1,1,2,2,2]
samples = [5x4 pd.DataFrame, 5x4 pd.DataFrame, 5x4 pd.DataFrame, 5x4 pd.DataFrame, 5x4 pd.DataFrame, 5x4 pd.DataFrame]
Desired Output:
index labels samples
0 1 1 2 3 4 5 6 7
3 5 6 7 9 5 4
3 4 5 6 7 8 9
1 1 4 3 2 4 5 6 7
3 5 6 7 4 5 6
2 3 4 3 4 5 3
...
1 answers
solution1
2 ACCPTED 2017-04-12 06:50:05
If select with not unique items, get another Panel :
np.random.seed(10)
labels = [1,1,1,2,2,2]
samples = np.random.randn(6, 5, 4)
p1 = pd.Panel(samples, items=map(str, labels))
print (p1)
<class 'pandas.core.panel.Panel'>
Dimensions: 6 (items) x 5 (major_axis) x 4 (minor_axis)
Items axis: 1 to 2
Major_axis axis: 0 to 4
Minor_axis axis: 0 to 3
print (p1['1'])
<class 'pandas.core.panel.Panel'>
Dimensions: 3 (items) x 5 (major_axis) x 4 (minor_axis)
Items axis: 1 to 1
Major_axis axis: 0 to 4
Minor_axis axis: 0 to 3
print (p1.to_frame())
1 1 1 2 2 2
major minor
0 0 1.331587 1.331587 1.331587 -0.232182 -0.232182 -0.232182
1 0.715279 0.715279 0.715279 -0.501729 -0.501729 -0.501729
2 -1.545400 -1.545400 -1.545400 1.128785 1.128785 1.128785
3 -0.008384 -0.008384 -0.008384 -0.697810 -0.697810 -0.697810
1 0 0.621336 0.621336 0.621336 -0.081122 -0.081122 -0.081122
1 -0.720086 -0.720086 -0.720086 -0.529296 -0.529296 -0.529296
2 0.265512 0.265512 0.265512 1.046183 1.046183 1.046183
3 0.108549 0.108549 0.108549 -1.418556 -1.418556 -1.418556
2 0 0.004291 0.004291 0.004291 -0.362499 -0.362499 -0.362499
1 -0.174600 -0.174600 -0.174600 -0.121906 -0.121906 -0.121906
2 0.433026 0.433026 0.433026 0.319356 0.319356 0.319356
3 1.203037 1.203037 1.203037 0.460903 0.460903 0.460903
3 0 -0.965066 -0.965066 -0.965066 -0.215790 -0.215790 -0.215790
1 1.028274 1.028274 1.028274 0.989072 0.989072 0.989072
2 0.228630 0.228630 0.228630 0.314754 0.314754 0.314754
3 0.445138 0.445138 0.445138 2.467651 2.467651 2.467651
4 0 -1.136602 -1.136602 -1.136602 -1.508321 -1.508321 -1.508321
1 0.135137 0.135137 0.135137 0.620601 0.620601 0.620601
2 1.484537 1.484537 1.484537 -1.045133 -1.045133 -1.045133
3 -1.079805 -1.079805 -1.079805 -0.798009 -0.798009 -0.798009
But if have unique one, get DataFrame :
np.random.seed(10)
labels = list('abcdef')
samples = np.random.randn(6, 5, 4)
p1 = pd.Panel(samples, items=labels)
print (p1)
<class 'pandas.core.panel.Panel'>
Dimensions: 6 (items) x 5 (major_axis) x 4 (minor_axis)
Items axis: a to f
Major_axis axis: 0 to 4
Minor_axis axis: 0 to 3
print (p1['a'])
0 1 2 3
0 1.331587 0.715279 -1.545400 -0.008384
1 0.621336 -0.720086 0.265512 0.108549
2 0.004291 -0.174600 0.433026 1.203037
3 -0.965066 1.028274 0.228630 0.445138
4 -1.136602 0.135137 1.484537 -1.079805
print (p1.to_frame())
a b c d e f
major minor
0 0 1.331587 -1.977728 0.660232 -0.232182 1.985085 0.117476
1 0.715279 -1.743372 -0.350872 -0.501729 1.744814 -1.907457
2 -1.545400 0.266070 -0.939433 1.128785 -1.856185 -0.922909
3 -0.008384 2.384967 -0.489337 -0.697810 -0.222774 0.469751
1 0 0.621336 1.123691 -0.804591 -0.081122 -0.065848 -0.144367
1 -0.720086 1.672622 -0.212698 -0.529296 -2.131712 -0.400138
2 0.265512 0.099149 -0.339140 1.046183 -0.048831 -0.295984
3 0.108549 1.397996 0.312170 -1.418556 0.393341 0.848209
2 0 0.004291 -0.271248 0.565153 -0.362499 0.217265 0.706830
1 -0.174600 0.613204 -0.147420 -0.121906 -1.994394 -0.787269
2 0.433026 -0.267317 -0.025905 0.319356 1.107708 0.292941
3 1.203037 -0.549309 0.289094 0.460903 0.244544 -0.470807
3 0 -0.965066 0.132708 -0.539879 -0.215790 -0.061912 2.404326
1 1.028274 -0.476142 0.708160 0.989072 -0.753893 -0.739357
2 0.228630 1.308473 0.842225 0.314754 0.711959 -0.312829
3 0.445138 0.195013 0.203581 2.467651 0.918269 -0.348882
4 0 -1.136602 0.400210 2.394704 -1.508321 -0.482093 -0.439026
1 0.135137 -0.337632 0.917459 0.620601 0.089588 0.141104
2 1.484537 1.256472 -0.112272 -1.045133 0.826999 0.273049
3 -1.079805 -0.731970 -0.362180 -0.798009 -1.954512 -1.618571
It is same as in DataFrame with non unique columns:
samples = np.random.randn(6, 5)
df = pd.DataFrame(samples, columns=list('11122'))
print (df)
1 1 1 2 2
0 0.346338 -0.855797 -0.932463 -2.289259 0.634696
1 0.272794 -0.924357 -1.898270 -0.743083 -1.587480
2 -0.519975 -0.136836 0.530178 -0.730629 2.520821
3 0.137530 -1.232763 0.508548 -0.480384 -1.213064
4 -0.157787 -1.600004 -1.287620 0.384642 -0.568072
5 -0.649427 -0.659585 -0.813359 -1.487412 -0.044206
print (df['1'])
1 1 1
0 0.346338 -0.855797 -0.932463
1 0.272794 -0.924357 -1.898270
2 -0.519975 -0.136836 0.530178
3 0.137530 -1.232763 0.508548
4 -0.157787 -1.600004 -1.287620
5 -0.649427 -0.659585 -0.813359
EDIT:
Also for creating df from list need unique labels (no unique raise error) and function concat with parameter keys , for Panel call to_panel :
np.random.seed(100)
raw_sample = []
labels = list('abcdef')
samples = np.random.randn(6, 5, 4)
for contents in range(samples.shape[0]):
raw_sample.append(pd.DataFrame(samples[contents]))
df = pd.concat(raw_sample, keys=labels)
print (df)
0 1 2 3
a 0 -1.749765 0.342680 1.153036 -0.252436
1 0.981321 0.514219 0.221180 -1.070043
2 -0.189496 0.255001 -0.458027 0.435163
3 -0.583595 0.816847 0.672721 -0.104411
4 -0.531280 1.029733 -0.438136 -1.118318
b 0 1.618982 1.541605 -0.251879 -0.842436
1 0.184519 0.937082 0.731000 1.361556
2 -0.326238 0.055676 0.222400 -1.443217
3 -0.756352 0.816454 0.750445 -0.455947
4 1.189622 -1.690617 -1.356399 -1.232435
c 0 -0.544439 -0.668172 0.007315 -0.612939
1 1.299748 -1.733096 -0.983310 0.357508
2 -1.613579 1.470714 -1.188018 -0.549746
3 -0.940046 -0.827932 0.108863 0.507810
4 -0.862227 1.249470 -0.079611 -0.889731
d 0 -0.881798 0.018639 0.237845 0.013549
1 -1.635529 -1.044210 0.613039 0.736205
2 1.026921 -1.432191 -1.841188 0.366093
3 -0.331777 -0.689218 2.034608 -0.550714
4 0.750453 -1.306992 0.580573 -1.104523
e 0 0.690121 0.686890 -1.566688 0.904974
1 0.778822 0.428233 0.108872 0.028284
2 -0.578826 -1.199451 -1.705952 0.369164
3 1.876573 -0.376903 1.831936 0.003017
4 -0.076023 0.003958 -0.185014 -2.487152
f 0 -1.704651 -1.136261 -2.973315 0.033317
1 -0.248889 -0.450176 0.132428 0.022214
2 0.317368 -0.752414 -1.296392 0.095139
3 -0.423715 -1.185984 -0.365462 -1.271023
4 1.586171 0.693391 -1.958081 -0.134801
p1 = df.to_panel()
print (p1)
<class 'pandas.core.panel.Panel'>
Dimensions: 4 (items) x 6 (major_axis) x 5 (minor_axis)
Items axis: 0 to 3
Major_axis axis: a to f
Minor_axis axis: 0 to 4
EDIT1:
If need MultiIndex DataFrame is possible create helper range for unique values, use concat and last remove helper level of MultiIndex :
np.random.seed(100)
raw_sample = []
labels = [1,1,1,2,2,2]
mux = pd.MultiIndex.from_arrays([labels, range(len(labels))])
samples = np.random.randn(6, 5, 4)
for contents in range(samples.shape[0]):
raw_sample.append(pd.DataFrame(samples[contents]))
df = pd.concat(raw_sample, keys=mux)
df = df.reset_index(level=1, drop=True)
print (df)
0 1 2 3
1 0 -1.749765 0.342680 1.153036 -0.252436
1 0.981321 0.514219 0.221180 -1.070043
2 -0.189496 0.255001 -0.458027 0.435163
3 -0.583595 0.816847 0.672721 -0.104411
4 -0.531280 1.029733 -0.438136 -1.118318
0 1.618982 1.541605 -0.251879 -0.842436
1 0.184519 0.937082 0.731000 1.361556
2 -0.326238 0.055676 0.222400 -1.443217
3 -0.756352 0.816454 0.750445 -0.455947
4 1.189622 -1.690617 -1.356399 -1.232435
0 -0.544439 -0.668172 0.007315 -0.612939
1 1.299748 -1.733096 -0.983310 0.357508
2 -1.613579 1.470714 -1.188018 -0.549746
3 -0.940046 -0.827932 0.108863 0.507810
4 -0.862227 1.249470 -0.079611 -0.889731
2 0 -0.881798 0.018639 0.237845 0.013549
1 -1.635529 -1.044210 0.613039 0.736205
2 1.026921 -1.432191 -1.841188 0.366093
3 -0.331777 -0.689218 2.034608 -0.550714
4 0.750453 -1.306992 0.580573 -1.104523
0 0.690121 0.686890 -1.566688 0.904974
1 0.778822 0.428233 0.108872 0.028284
2 -0.578826 -1.199451 -1.705952 0.369164
3 1.876573 -0.376903 1.831936 0.003017
4 -0.076023 0.003958 -0.185014 -2.487152
0 -1.704651 -1.136261 -2.973315 0.033317
1 -0.248889 -0.450176 0.132428 0.022214
2 0.317368 -0.752414 -1.296392 0.095139
3 -0.423715 -1.185984 -0.365462 -1.271023
4 1.586171 0.693391 -1.958081 -0.134801
But create panel is not possible:
p1 = df.to_panel()
print (p1)
>ValueError: Can't convert non-uniquely indexed DataFrame to Panel
The technical post webpages of this site follow the CC BY-SA 4.0 protocol. If you need to reprint, please indicate the site URL or the original address.Any question please contact:yoyou2525@163.com.
Related Question
Converting several 2D DataFrames into one 3D DataFrame
convert 2D dataframe to 3D dataframe
convert 2D dataframe to 3D dataframe
Plotting Panda Dataframe into 3D bar chart
Converting 3D xarray dataset to dataframe
Extract values form two 2D list and store them into a panda dataframe
Converting a 2D numpy array to dataframe rows
How to convert 3d np array to 2d for pandas dataframe
Converting a 2D List to a 3D List in Python
Matplotlib 3D surface plot from 2D pandas dataframe
Related Tags