February 2, 2022

Grouping Data: A Step-by-Step Tutorial to GroupBy in Pandas

In this tutorial, we will explore how to create a GroupBy object in pandas library of Python and how this object works. We will take a detailed look at each step of a grouping process, what methods can be applied to a GroupBy object, and what information we can extract from it.

The 3 Steps of a Groupby Process

Any groupby process involves some combination of the following 3 steps:

  • Splitting the original object into groups based on the defined criteria.
  • Applying a function to each group.
  • Combining the results.

Let’s explore this split-apply-combine chain step-by-step with an example from a Kaggle Nobel Prize Dataset:

import pandas as pd
import numpy as np

pd.set_option('max_columns', None)

df = pd.read_csv('complete.csv')
df = df[['awardYear', 'category', 'prizeAmount', 'prizeAmountAdjusted', 'name', 'gender', 'birth_continent']]
df.head()
awardYear category prizeAmount prizeAmountAdjusted name gender birth_continent
0 2001 Economic Sciences 10000000 12295082 A. Michael Spence male North America
1 1975 Physics 630000 3404179 Aage N. Bohr male Europe
2 2004 Chemistry 10000000 11762861 Aaron Ciechanover male Asia
3 1982 Chemistry 1150000 3102518 Aaron Klug male Europe
4 1979 Physics 800000 2988048 Abdus Salam male Asia

Splitting the Original Object into Groups

At this stage, we call the pandas DataFrame.groupby() function. We use it to split the data into groups based on predefined criteria, along rows (by default, axis=0), or columns (axis=1). In other words, this function maps the labels to the names of the groups.

For example, in our case, we can group the data on Nobel prizes by prize category:

grouped = df.groupby('category')

It is also possible to use more than one column to perform data grouping, passing a list of columns. Let’s group our data first by prize category and then, inside each of the created groups, we will apply additional grouping based on the year of the prize award:

grouped_category_year = df.groupby(['category', 'awardYear'])

Now, if we try to print one of the two GroupBy objects that we created, we will actually see no groups:

print(grouped)
<pandas.core.groupby.generic.DataFrameGroupBy object at 0x0000026083789DF0>

It is important to note that creating a GroupBy object only checks if we have passed a correct mapping; it doesn’t really perform any of the operations of the split-apply-combine chain until we explicitly use some method on this object or extract some of its attributes.

To briefly inspect the resulting GroupBy object and check how exactly the groups were split, we can extract from it the groups or indices attribute. Both of them return a dictionary where the keys are the created groups and the values are the lists of axis labels (for the groups attribute) or indices (for the indices attribute) of the instances of each group in the original DataFrame:

grouped.indices
{'Chemistry': array([  2,   3,   7,   9,  10,  11,  13,  14,  15,  17,  19,  39,  62,
         64,  66,  71,  75,  80,  81,  86,  92, 104, 107, 112, 129, 135,
        153, 169, 175, 178, 181, 188, 197, 199, 203, 210, 215, 223, 227,
        239, 247, 249, 258, 264, 265, 268, 272, 274, 280, 282, 284, 289,
        296, 298, 310, 311, 317, 318, 337, 341, 343, 348, 352, 357, 362,
        365, 366, 372, 374, 384, 394, 395, 396, 415, 416, 419, 434, 440,
        442, 444, 446, 448, 450, 455, 456, 459, 461, 463, 465, 469, 475,
        504, 505, 508, 518, 522, 523, 524, 539, 549, 558, 559, 563, 567,
        571, 572, 585, 591, 596, 599, 627, 630, 632, 641, 643, 644, 648,
        659, 661, 666, 667, 668, 671, 673, 679, 681, 686, 713, 715, 717,
        719, 720, 722, 723, 725, 726, 729, 732, 738, 742, 744, 746, 751,
        756, 759, 763, 766, 773, 776, 798, 810, 813, 814, 817, 827, 828,
        829, 832, 839, 848, 853, 855, 862, 866, 880, 885, 886, 888, 889,
        892, 894, 897, 902, 904, 914, 915, 920, 921, 922, 940, 941, 943,
        946, 947], dtype=int64),
 'Economic Sciences': array([  0,   5,  45,  46,  58,  90,  96, 139, 140, 145, 152, 156, 157,
        180, 187, 193, 207, 219, 231, 232, 246, 250, 269, 279, 283, 295,
        305, 324, 346, 369, 418, 422, 425, 426, 430, 432, 438, 458, 467,
        476, 485, 510, 525, 527, 537, 538, 546, 580, 594, 595, 605, 611,
        636, 637, 657, 669, 670, 678, 700, 708, 716, 724, 734, 737, 739,
        745, 747, 749, 750, 753, 758, 767, 800, 805, 854, 856, 860, 864,
        871, 882, 896, 912, 916, 924], dtype=int64),
 'Literature': array([ 21,  31,  40,  49,  52,  98, 100, 101, 102, 111, 115, 142, 149,
        159, 170, 177, 201, 202, 220, 221, 233, 235, 237, 253, 257, 259,
        275, 277, 278, 286, 312, 315, 316, 321, 326, 333, 345, 347, 350,
        355, 359, 364, 370, 373, 385, 397, 400, 403, 406, 411, 435, 439,
        441, 454, 468, 479, 480, 482, 483, 492, 501, 506, 511, 516, 556,
        569, 581, 602, 604, 606, 613, 614, 618, 631, 633, 635, 640, 652,
        653, 655, 656, 665, 675, 683, 699, 761, 765, 771, 774, 777, 779,
        780, 784, 786, 788, 796, 799, 803, 836, 840, 842, 850, 861, 867,
        868, 878, 881, 883, 910, 917, 919, 927, 928, 929, 930, 936],
       dtype=int64),
 'Peace': array([  6,  12,  16,  25,  26,  27,  34,  36,  44,  47,  48,  54,  61,
         65,  72,  78,  79,  82,  95,  99, 116, 119, 120, 126, 137, 146,
        151, 166, 167, 171, 200, 204, 205, 206, 209, 213, 225, 236, 240,
        244, 255, 260, 266, 267, 270, 287, 303, 320, 329, 356, 360, 361,
        377, 386, 387, 388, 389, 390, 391, 392, 393, 433, 447, 449, 471,
        477, 481, 489, 491, 500, 512, 514, 517, 528, 529, 530, 533, 534,
        540, 542, 544, 545, 547, 553, 555, 560, 562, 574, 578, 590, 593,
        603, 607, 608, 609, 612, 615, 616, 617, 619, 620, 628, 634, 639,
        642, 664, 677, 688, 697, 703, 705, 710, 727, 736, 787, 793, 795,
        806, 823, 846, 847, 852, 865, 875, 876, 877, 895, 926, 934, 935,
        937, 944, 948, 949], dtype=int64),
 'Physics': array([  1,   4,   8,  20,  23,  24,  30,  32,  38,  51,  59,  60,  67,
         68,  69,  70,  74,  84,  89,  97, 103, 105, 108, 109, 114, 117,
        118, 122, 125, 127, 128, 130, 133, 141, 143, 144, 155, 162, 163,
        164, 165, 168, 173, 174, 176, 179, 183, 195, 212, 214, 216, 222,
        224, 228, 230, 234, 238, 241, 243, 251, 256, 263, 271, 276, 291,
        292, 297, 301, 306, 307, 308, 323, 327, 328, 330, 335, 336, 338,
        349, 351, 353, 354, 363, 367, 375, 376, 378, 381, 382, 398, 399,
        402, 404, 405, 408, 410, 412, 413, 420, 421, 424, 428, 429, 436,
        445, 451, 453, 457, 460, 462, 470, 472, 487, 495, 498, 499, 509,
        513, 515, 521, 526, 532, 535, 536, 541, 548, 550, 552, 557, 561,
        564, 565, 566, 573, 576, 577, 579, 583, 586, 588, 592, 601, 610,
        621, 622, 623, 629, 647, 650, 651, 654, 658, 674, 676, 682, 684,
        690, 691, 693, 694, 695, 696, 698, 702, 707, 711, 714, 721, 730,
        731, 735, 743, 752, 755, 770, 772, 775, 781, 785, 790, 792, 797,
        801, 802, 808, 822, 833, 834, 835, 844, 851, 870, 872, 879, 884,
        887, 890, 893, 900, 901, 903, 905, 907, 908, 909, 913, 925, 931,
        932, 933, 938, 942, 945], dtype=int64),
 'Physiology or Medicine': array([ 18,  22,  28,  29,  33,  35,  37,  41,  42,  43,  50,  53,  55,
         56,  57,  63,  73,  76,  77,  83,  85,  87,  88,  91,  93,  94,
        106, 110, 113, 121, 123, 124, 131, 132, 134, 136, 138, 147, 148,
        150, 154, 158, 160, 161, 172, 182, 184, 185, 186, 189, 190, 191,
        192, 194, 196, 198, 208, 211, 217, 218, 226, 229, 242, 245, 248,
        252, 254, 261, 262, 273, 281, 285, 288, 290, 293, 294, 299, 300,
        302, 304, 309, 313, 314, 319, 322, 325, 331, 332, 334, 339, 340,
        342, 344, 358, 368, 371, 379, 380, 383, 401, 407, 409, 414, 417,
        423, 427, 431, 437, 443, 452, 464, 466, 473, 474, 478, 484, 486,
        488, 490, 493, 494, 496, 497, 502, 503, 507, 519, 520, 531, 543,
        551, 554, 568, 570, 575, 582, 584, 587, 589, 597, 598, 600, 624,
        625, 626, 638, 645, 646, 649, 660, 662, 663, 672, 680, 685, 687,
        689, 692, 701, 704, 706, 709, 712, 718, 728, 733, 740, 741, 748,
        754, 757, 760, 762, 764, 768, 769, 778, 782, 783, 789, 791, 794,
        804, 807, 809, 811, 812, 815, 816, 818, 819, 820, 821, 824, 825,
        826, 830, 831, 837, 838, 841, 843, 845, 849, 857, 858, 859, 863,
        869, 873, 874, 891, 898, 899, 906, 911, 918, 923, 939], dtype=int64)}

To find the number of groups in a GroupBy object, we can extract from it the ngroups attribute or call the len function of the Python standard library:

print(grouped.ngroups)
print(len(grouped))
6
6

If we need to visualize all or some entries of each group, we can iterate over the GroupBy object:

for name, entries in grouped:
    print(f'First 2 entries for the "{name}" category:')
    print(30*'-')
    print(entries.head(2), '\n\n')
First 2 entries for the "Chemistry" category:
------------------------------
   awardYear   category  prizeAmount  prizeAmountAdjusted               name  \
2       2004  Chemistry     10000000             11762861  Aaron Ciechanover   
3       1982  Chemistry      1150000              3102518         Aaron Klug   

  gender birth_continent  
2   male            Asia  
3   male          Europe   

First 2 entries for the "Economic Sciences" category:
------------------------------
   awardYear           category  prizeAmount  prizeAmountAdjusted  \
0       2001  Economic Sciences     10000000             12295082   
5       2019  Economic Sciences      9000000              9000000   

                name gender birth_continent  
0  A. Michael Spence   male   North America  
5   Abhijit Banerjee   male            Asia   

First 2 entries for the "Literature" category:
------------------------------
    awardYear    category  prizeAmount  prizeAmountAdjusted  \
21       1957  Literature       208629              2697789   
31       1970  Literature       400000              3177966   

                     name gender birth_continent  
21           Albert Camus   male          Africa  
31  Alexandr Solzhenitsyn   male          Europe   

First 2 entries for the "Peace" category:
------------------------------
    awardYear category  prizeAmount  prizeAmountAdjusted  \
6        2019    Peace      9000000              9000000   
12       1980    Peace       880000              2889667   

                     name gender birth_continent  
6          Abiy Ahmed Ali   male          Africa  
12  Adolfo Pérez Esquivel   male   South America   

First 2 entries for the "Physics" category:
------------------------------
   awardYear category  prizeAmount  prizeAmountAdjusted          name gender  \
1       1975  Physics       630000              3404179  Aage N. Bohr   male   
4       1979  Physics       800000              2988048   Abdus Salam   male   

  birth_continent  
1          Europe  
4            Asia   

First 2 entries for the "Physiology or Medicine" category:
------------------------------
    awardYear                category  prizeAmount  prizeAmountAdjusted  \
18       1963  Physiology or Medicine       265000              2839286   
22       1974  Physiology or Medicine       550000              3263449   

             name gender birth_continent  
18   Alan Hodgkin   male          Europe  
22  Albert Claude   male          Europe   

If instead, we want to select a single group in a form of DataFrame, we should use the method get_group() on the GroupBy object:

grouped.get_group('Economic Sciences')
awardYear category prizeAmount prizeAmountAdjusted name gender birth_continent
0 2001 Economic Sciences 10000000 12295082 A. Michael Spence male North America
5 2019 Economic Sciences 9000000 9000000 Abhijit Banerjee male Asia
45 2012 Economic Sciences 8000000 8361204 Alvin E. Roth male North America
46 1998 Economic Sciences 7600000 9713701 Amartya Sen male Asia
58 2015 Economic Sciences 8000000 8384572 Angus Deaton male Europe
882 2002 Economic Sciences 10000000 12034660 Vernon L. Smith male North America
896 1973 Economic Sciences 510000 3331882 Wassily Leontief male Europe
912 2018 Economic Sciences 9000000 9000000 William D. Nordhaus male North America
916 1990 Economic Sciences 4000000 6329114 William F. Sharpe male North America
924 1996 Economic Sciences 7400000 9490424 William Vickrey male North America

84 rows × 7 columns

Applying a Function by Group

After splitting the original data and (optionally) inspecting the resulting groups, we can perform one of the following operations or their combination (not necessarily in the given order) on each group:

  • Aggregation: calculating a summary statistic for each group (e.g., group sizes, means, medians, or sums) and outputting a single number for many data points.
  • Transformation: conducting some operations by group, such as calculating the z-score for each group.
  • Filtration: rejecting some groups based on a predefined condition, such as group size, mean, median, or sum. This can also include filtering out particular rows from each group.

Aggregation

To aggregate the data of a GroupBy object (i.e., to calculate a summary statistic by group), we can use the agg() method on the object:

# Showing only 1 decimal for all float numbers
pd.options.display.float_format = '{:.1f}'.format

grouped.agg(np.mean)
awardYear prizeAmount prizeAmountAdjusted
category
Chemistry 1972.3 3629279.4 6257868.1
Economic Sciences 1996.1 6105845.2 7837779.2
Literature 1960.9 2493811.2 5598256.3
Peace 1964.5 3124879.2 6163906.9
Physics 1971.1 3407938.6 6086978.2
Physiology or Medicine 1970.4 3072972.9 5738300.7

The code above produces a DataFrame with the group names as its new index and the mean values for each numeric column by group.

Instead of using the agg() method, we can apply the corresponding pandas method directly on a GroupBy object. The most common methods are mean(), median(), mode(), sum(), size(), count(), min(), max(), std(), var() (computes the variance of each group), describe() (outputs descriptive statistics by group), and nunique() (gives the number of unique values in each group).

grouped.sum()
awardYear prizeAmount prizeAmountAdjusted
category
Chemistry 362912 667787418 1151447726
Economic Sciences 167674 512891000 658373449
Literature 227468 289282102 649397731
Peace 263248 418733807 825963521
Physics 419837 725890928 1296526352
Physiology or Medicine 431508 672981066 1256687857

Usually, we are interested only in the statistics for some particular column or columns, so we need to specify it (or them). In the example above, we definitely don’t want to sum all the years. Instead, we may want to sum the prize values by prize category. For this purpose, we can select the prizeAmountAdjusted column of the GroupBy object, just like we select a column of a DataFrame, and apply the sum() function on it:

grouped['prizeAmountAdjusted'].sum()
category
Chemistry                 1151447726
Economic Sciences          658373449
Literature                 649397731
Peace                      825963521
Physics                   1296526352
Physiology or Medicine    1256687857
Name: prizeAmountAdjusted, dtype: int64

For the piece of code above (as well as for some of the next examples), we can use an equivalent syntax applying the function on a GroupBy object before selecting the necessary column: grouped.sum()['prizeAmountAdjusted']. However, the previous syntax is preferable since it performs better, especially on large datasets.

If we need to aggregate data for two or more columns, we use double square brackets:

grouped[['prizeAmount', 'prizeAmountAdjusted']].sum()
prizeAmount prizeAmountAdjusted
category
Chemistry 667787418 1151447726
Economic Sciences 512891000 658373449
Literature 289282102 649397731
Peace 418733807 825963521
Physics 725890928 1296526352
Physiology or Medicine 672981066 1256687857

It is possible to apply several functions at once to one or more columns of a GroupBy object. For this purpose, we again need the agg() method and a list of the functions of interest:

grouped[['prizeAmount', 'prizeAmountAdjusted']].agg([np.sum, np.mean, np.std])
prizeAmount prizeAmountAdjusted
sum mean std sum mean std
category
Chemistry 667787418 3629279.4 4070588.4 1151447726 6257868.1 3276027.2
Economic Sciences 512891000 6105845.2 3787630.1 658373449 7837779.2 3313153.2
Literature 289282102 2493811.2 3653734.0 649397731 5598256.3 3029512.1
Peace 418733807 3124879.2 3934390.9 825963521 6163906.9 3189886.1
Physics 725890928 3407938.6 4013073.0 1296526352 6086978.2 3294268.5
Physiology or Medicine 672981066 3072972.9 3898539.3 1256687857 5738300.7 3241781.0

Also, we can consider applying different aggregation functions to different columns of a GroupBy object by passing a dictionary:

grouped.agg({'prizeAmount': [np.sum, np.size], 'prizeAmountAdjusted': np.mean})
prizeAmount prizeAmountAdjusted
sum size mean
category
Chemistry 667787418 184 6257868.1
Economic Sciences 512891000 84 7837779.2
Literature 289282102 116 5598256.3
Peace 418733807 134 6163906.9
Physics 725890928 213 6086978.2
Physiology or Medicine 672981066 219 5738300.7

Transformation

Unlike aggregation methods (and also unlike filtration ones, as we will see soon), transformation methods return a new DataFrame with the same shape and indexing as the original one but with transformed individual values. Here it is important to note that the transformation must not modify any values in the original DataFrame, meaning that such operations cannot be performed in place.

The most common pandas method to transform the data of a GroupBy object is transform(). For example, it can be helpful for computing the z-score for each group:

grouped[['prizeAmount', 'prizeAmountAdjusted']].transform(lambda x: (x - x.mean()) / x.std())
prizeAmount prizeAmountAdjusted
0 1.0 1.3
1 -0.7 -0.8
2 1.6 1.7
3 -0.6 -1.0
4 -0.6 -0.9
945 -0.7 -0.8
946 -0.8 -1.1
947 -0.9 0.3
948 -0.5 -1.0
949 -0.7 -1.0

950 rows × 2 columns

With transformation methods, we can also replace missing data with the group mean, median, mode, or any other value:

grouped['gender'].transform(lambda x: x.fillna(x.mode()[0]))
0        male
1        male
2        male
3        male
4        male
        ...  
945      male
946      male
947    female
948      male
949      male
Name: gender, Length: 950, dtype: object

There are some other pandas methods that we can use to transform the data of a GroupBy object: bfill(), ffill(), diff(), pct_change(), rank(), shift(), quantile(), etc.

Filtration

Filtration methods discard the groups or particular rows from each group based on a predefined condition and return a subset of the original data. For example, we may want to keep only the values of a certain column from all the groups where the group mean for that column is greater than a predefined value. In the case of our DataFrame, let’s filter out all the groups with a group mean for the prizeAmountAdjusted column smaller than 7,000,000, and keep only this column in the output:

grouped['prizeAmountAdjusted'].filter(lambda x: x.mean() > 7000000)
0      12295082
5       9000000
45      8361204
46      9713701
58      8384572
         ...   
882    12034660
896     3331882
912     9000000
916     6329114
924     9490424
Name: prizeAmountAdjusted, Length: 84, dtype: int64

Another example is filtering out the groups with more than a certain number of elements:

grouped['prizeAmountAdjusted'].filter(lambda x: len(x) < 100)
0      12295082
5       9000000
45      8361204
46      9713701
58      8384572
         ...   
882    12034660
896     3331882
912     9000000
916     6329114
924     9490424
Name: prizeAmountAdjusted, Length: 84, dtype: int64

In both the operations above, we used the filter() method passing a lambda function as an argument. Such a function, applied to the entire group, returns True or False based on the result of comparison of that group with predifined statistical condition. In other words, the function inside the filter() method decides which groups to keep in the new dataframe.

Along with filtering out the whole groups, it is also possible to discard certain rows from each group. Some helpful methods here are first(), last(), and nth(). Applying one of them on a GroupBy object returns the first/last/nth entry of each group correspondingly:

grouped.last()
awardYear prizeAmount prizeAmountAdjusted name gender birth_continent
category
Chemistry 1911 140695 7327865 Marie Curie female Europe
Economic Sciences 1996 7400000 9490424 William Vickrey male North America
Literature 1968 350000 3052326 Yasunari Kawabata male Asia
Peace 1963 265000 2839286 International Committee of the Red Cross male Asia
Physics 1972 480000 3345725 John Bardeen male North America
Physiology or Medicine 2016 8000000 8301051 Yoshinori Ohsumi male Asia

For the nth() method, we have to pass the integer representing the index of entry we want to return for each group:

grouped.nth(1)
awardYear prizeAmount prizeAmountAdjusted name gender birth_continent
category
Chemistry 1982 1150000 3102518 Aaron Klug male Europe
Economic Sciences 2019 9000000 9000000 Abhijit Banerjee male Asia
Literature 1970 400000 3177966 Alexandr Solzhenitsyn male Europe
Peace 1980 880000 2889667 Adolfo Pérez Esquivel male South America
Physics 1979 800000 2988048 Abdus Salam male Asia
Physiology or Medicine 1974 550000 3263449 Albert Claude male Europe

The piece of code above gathers the second entries of all the groups, keeping in mind the 0-indexing in Python.

Two more methods for filtering out the rows in each group are head() and tail(), returning the first/last n rows (5, by default) of each group correspondingly:

grouped.head(3)
awardYear category prizeAmount prizeAmountAdjusted name gender birth_continent
0 2001 Economic Sciences 10000000 12295082 A. Michael Spence male North America
1 1975 Physics 630000 3404179 Aage N. Bohr male Europe
2 2004 Chemistry 10000000 11762861 Aaron Ciechanover male Asia
3 1982 Chemistry 1150000 3102518 Aaron Klug male Europe
4 1979 Physics 800000 2988048 Abdus Salam male Asia
5 2019 Economic Sciences 9000000 9000000 Abhijit Banerjee male Asia
6 2019 Peace 9000000 9000000 Abiy Ahmed Ali male Africa
7 2009 Chemistry 10000000 10958504 Ada E. Yonath female Asia
8 2011 Physics 10000000 10545557 Adam G. Riess male North America
12 1980 Peace 880000 2889667 Adolfo Pérez Esquivel male South America
16 2007 Peace 10000000 11301989 Al Gore male North America
18 1963 Physiology or Medicine 265000 2839286 Alan Hodgkin male Europe
21 1957 Literature 208629 2697789 Albert Camus male Africa
22 1974 Physiology or Medicine 550000 3263449 Albert Claude male Europe
28 1937 Physiology or Medicine 158463 4716161 Albert Szent-Györgyi male Europe
31 1970 Literature 400000 3177966 Alexandr Solzhenitsyn male Europe
40 2013 Literature 8000000 8365867 Alice Munro female North America
45 2012 Economic Sciences 8000000 8361204 Alvin E. Roth male North America

Combining the Results

The last stage of the split-apply-combine chain — combining the results — is performed under the hood by pandas. It consists of taking the outputs of all of the operations conducted on a GroupBy object and uniting them back together, producing a new data structure, such as a Series or DataFrame. Assigning this data structure to a variable, we can use it to solve other tasks.

Conclusion

In this tutorial, we covered many aspects of using the pandas groupby function and working with the resulting object. In particular, we learned the following:

  • The steps a grouping process includes
  • How the split-apply-combine chain works, step-by-step
  • How to create a GroupBy object
  • How to briefly inspect a GroupBy object
  • The attributes of a GroupBy object
  • The operations that can be applied on a GroupBy object
  • How to calculate summary statistics by group and what methods are available for this purpose
  • How to apply several functions at once to one or more columns of a GroupBy object
  • How to apply different aggregation functions to different columns of a GroupBy object
  • How and why to transform the values from the original DataFrame
  • How to filter the groups of a GroupBy object or particular rows of each group
  • How pandas combines the results of a grouping process
  • The data structures a grouping process produces

With this information, you’re ready to start applying your skills in pandas groupby.

Elena Kosourova

About the author

Elena Kosourova

Elena is a Petroleum Geologist and community manager at Dataquest. You can find her chatting online with data enthusiasts and writing Python tutorials.

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