What’s new in 1.1.0 (July 28, 2020)

These are the changes in pandas 1.1.0. See Release notes for a full changelog including other versions of pandas.

Enhancements

KeyErrors raised by loc specify missing labels

Previously, if labels were missing for a .loc call, a KeyError was raised stating that this was no longer supported.

Now the error message also includes a list of the missing labels (max 10 items, display width 80 characters). See GH34272.

All dtypes can now be converted to StringDtype

Previously, declaring or converting to StringDtype was in general only possible if the data was already only str or nan-like (GH31204). StringDtype now works in all situations where astype(str) or dtype=str work:

For example, the below now works:

In [1]: ser = pd.Series([1, "abc", np.nan], dtype="string")

In [2]: ser
Out[2]: 
0       1
1     abc
2    <NA>
dtype: string

In [3]: ser[0]
Out[3]: '1'

In [4]: pd.Series([1, 2, np.nan], dtype="Int64").astype("string")
Out[4]: 
0       1
1       2
2    <NA>
dtype: string

Non-monotonic PeriodIndex partial string slicing

PeriodIndex now supports partial string slicing for non-monotonic indexes, mirroring DatetimeIndex behavior (GH31096)

For example:

In [5]: dti = pd.date_range("2014-01-01", periods=30, freq="30D")

In [6]: pi = dti.to_period("D")

In [7]: ser_monotonic = pd.Series(np.arange(30), index=pi)

In [8]: shuffler = list(range(0, 30, 2)) + list(range(1, 31, 2))

In [9]: ser = ser_monotonic[shuffler]

In [10]: ser
Out[10]: 
2014-01-01     0
2014-03-02     2
2014-05-01     4
2014-06-30     6
2014-08-29     8
              ..
2015-09-23    21
2015-11-22    23
2016-01-21    25
2016-03-21    27
2016-05-20    29
Freq: D, Length: 30, dtype: int64

In [11]: ser["2014"]
Out[11]: 
2014-01-01     0
2014-03-02     2
2014-05-01     4
2014-06-30     6
2014-08-29     8
2014-10-28    10
2014-12-27    12
2014-01-31     1
2014-04-01     3
2014-05-31     5
2014-07-30     7
2014-09-28     9
2014-11-27    11
Freq: D, dtype: int64

In [12]: ser.loc["May 2015"]
Out[12]: 
2015-05-26    17
Freq: D, dtype: int64

Comparing two DataFrame or two Series and summarizing the differences

We’ve added DataFrame.compare() and Series.compare() for comparing two DataFrame or two Series (GH30429)

In [13]: df = pd.DataFrame(
   ....:     {
   ....:         "col1": ["a", "a", "b", "b", "a"],
   ....:         "col2": [1.0, 2.0, 3.0, np.nan, 5.0],
   ....:         "col3": [1.0, 2.0, 3.0, 4.0, 5.0]
   ....:     },
   ....:     columns=["col1", "col2", "col3"],
   ....: )
   ....: 

In [14]: df
Out[14]: 
  col1  col2  col3
0    a   1.0   1.0
1    a   2.0   2.0
2    b   3.0   3.0
3    b   NaN   4.0
4    a   5.0   5.0

In [15]: df2 = df.copy()

In [16]: df2.loc[0, 'col1'] = 'c'

In [17]: df2.loc[2, 'col3'] = 4.0

In [18]: df2
Out[18]: 
  col1  col2  col3
0    c   1.0   1.0
1    a   2.0   2.0
2    b   3.0   4.0
3    b   NaN   4.0
4    a   5.0   5.0

In [19]: df.compare(df2)
Out[19]: 
  col1       col3      
  self other self other
0    a     c  NaN   NaN
2  NaN   NaN  3.0   4.0

See User Guide for more details.

Allow NA in groupby key

With groupby , we’ve added a dropna keyword to DataFrame.groupby() and Series.groupby() in order to allow NA values in group keys. Users can define dropna to False if they want to include NA values in groupby keys. The default is set to True for dropna to keep backwards compatibility (GH3729)

In [20]: df_list = [[1, 2, 3], [1, None, 4], [2, 1, 3], [1, 2, 2]]

In [21]: df_dropna = pd.DataFrame(df_list, columns=["a", "b", "c"])

In [22]: df_dropna
Out[22]: 
   a    b  c
0  1  2.0  3
1  1  NaN  4
2  2  1.0  3
3  1  2.0  2

# Default ``dropna`` is set to True, which will exclude NaNs in keys
In [23]: df_dropna.groupby(by=["b"], dropna=True).sum()
Out[23]: 
     a  c
b        
1.0  2  3
2.0  2  5

# In order to allow NaN in keys, set ``dropna`` to False
In [24]: df_dropna.groupby(by=["b"], dropna=False).sum()
Out[24]: 
     a  c
b        
1.0  2  3
2.0  2  5
NaN  1  4

The default setting of dropna argument is True which means NA are not included in group keys.

Sorting with keys

We’ve added a key argument to the DataFrame and Series sorting methods, including DataFrame.sort_values(), DataFrame.sort_index(), Series.sort_values(), and Series.sort_index(). The key can be any callable function which is applied column-by-column to each column used for sorting, before sorting is performed (GH27237). See sort_values with keys and sort_index with keys for more information.

In [25]: s = pd.Series(['C', 'a', 'B'])

In [26]: s
Out[26]: 
0    C
1    a
2    B
dtype: object

In [27]: s.sort_values()
Out[27]: 
2    B
0    C
1    a
dtype: object

Note how this is sorted with capital letters first. If we apply the Series.str.lower() method, we get

In [28]: s.sort_values(key=lambda x: x.str.lower())
Out[28]: 
1    a
2    B
0    C
dtype: object

When applied to a DataFrame, they key is applied per-column to all columns or a subset if by is specified, e.g.

In [29]: df = pd.DataFrame({'a': ['C', 'C', 'a', 'a', 'B', 'B'],
   ....:                    'b': [1, 2, 3, 4, 5, 6]})
   ....: 

In [30]: df
Out[30]: 
   a  b
0  C  1
1  C  2
2  a  3
3  a  4
4  B  5
5  B  6

In [31]: df.sort_values(by=['a'], key=lambda col: col.str.lower())
Out[31]: 
   a  b
2  a  3
3  a  4
4  B  5
5  B  6
0  C  1
1  C  2

For more details, see examples and documentation in DataFrame.sort_values(), Series.sort_values(), and sort_index().

Fold argument support in Timestamp constructor

Timestamp: now supports the keyword-only fold argument according to PEP 495 similar to parent datetime.datetime class. It supports both accepting fold as an initialization argument and inferring fold from other constructor arguments (GH25057, GH31338). Support is limited to dateutil timezones as pytz doesn’t support fold.

For example:

In [32]: ts = pd.Timestamp("2019-10-27 01:30:00+00:00")

In [33]: ts.fold
Out[33]: 0

In [34]: ts = pd.Timestamp(year=2019, month=10, day=27, hour=1, minute=30,
   ....:                   tz="dateutil/Europe/London", fold=1)
   ....: 

In [35]: ts
Out[35]: Timestamp('2019-10-27 01:30:00+0000', tz='dateutil//usr/share/zoneinfo/Europe/London')

For more on working with fold, see Fold subsection in the user guide.

Parsing timezone-aware format with different timezones in to_datetime

to_datetime() now supports parsing formats containing timezone names (%Z) and UTC offsets (%z) from different timezones then converting them to UTC by setting utc=True. This would return a DatetimeIndex with timezone at UTC as opposed to an Index with object dtype if utc=True is not set (GH32792).

For example:

In [36]: tz_strs = ["2010-01-01 12:00:00 +0100", "2010-01-01 12:00:00 -0100",
   ....:            "2010-01-01 12:00:00 +0300", "2010-01-01 12:00:00 +0400"]
   ....: 

In [37]: pd.to_datetime(tz_strs, format='%Y-%m-%d %H:%M:%S %z', utc=True)
Out[37]: 
DatetimeIndex(['2010-01-01 11:00:00+00:00', '2010-01-01 13:00:00+00:00',
               '2010-01-01 09:00:00+00:00', '2010-01-01 08:00:00+00:00'],
              dtype='datetime64[ns, UTC]', freq=None)

In [38]: pd.to_datetime(tz_strs, format='%Y-%m-%d %H:%M:%S %z')
Out[38]: 
Index([2010-01-01 12:00:00+01:00, 2010-01-01 12:00:00-01:00,
       2010-01-01 12:00:00+03:00, 2010-01-01 12:00:00+04:00],
      dtype='object')

Grouper and resample now supports the arguments origin and offset

Grouper and DataFrame.resample() now supports the arguments origin and offset. It let the user control the timestamp on which to adjust the grouping. (GH31809)

The bins of the grouping are adjusted based on the beginning of the day of the time series starting point. This works well with frequencies that are multiples of a day (like 30D) or that divides a day (like 90s or 1min). But it can create inconsistencies with some frequencies that do not meet this criteria. To change this behavior you can now specify a fixed timestamp with the argument origin.

Two arguments are now deprecated (more information in the documentation of DataFrame.resample()):

  • base should be replaced by offset.

  • loffset should be replaced by directly adding an offset to the index DataFrame after being resampled.

Small example of the use of origin:

In [39]: start, end = '2000-10-01 23:30:00', '2000-10-02 00:30:00'

In [40]: middle = '2000-10-02 00:00:00'

In [41]: rng = pd.date_range(start, end, freq='7min')

In [42]: ts = pd.Series(np.arange(len(rng)) * 3, index=rng)

In [43]: ts
Out[43]: 
2000-10-01 23:30:00     0
2000-10-01 23:37:00     3
2000-10-01 23:44:00     6
2000-10-01 23:51:00     9
2000-10-01 23:58:00    12
2000-10-02 00:05:00    15
2000-10-02 00:12:00    18
2000-10-02 00:19:00    21
2000-10-02 00:26:00    24
Freq: 7T, dtype: int64

Resample with the default behavior 'start_day' (origin is 2000-10-01 00:00:00):

In [44]: ts.resample('17min').sum()
Out[44]: 
2000-10-01 23:14:00     0
2000-10-01 23:31:00     9
2000-10-01 23:48:00    21
2000-10-02 00:05:00    54
2000-10-02 00:22:00    24
Freq: 17T, dtype: int64

In [45]: ts.resample('17min', origin='start_day').sum()
Out[45]: 
2000-10-01 23:14:00     0
2000-10-01 23:31:00     9
2000-10-01 23:48:00    21
2000-10-02 00:05:00    54
2000-10-02 00:22:00    24
Freq: 17T, dtype: int64

Resample using a fixed origin:

In [46]: ts.resample('17min', origin='epoch').sum()
Out[46]: 
2000-10-01 23:18:00     0
2000-10-01 23:35:00    18
2000-10-01 23:52:00    27
2000-10-02 00:09:00    39
2000-10-02 00:26:00    24
Freq: 17T, dtype: int64

In [47]: ts.resample('17min', origin='2000-01-01').sum()
Out[47]: 
2000-10-01 23:24:00     3
2000-10-01 23:41:00    15
2000-10-01 23:58:00    45
2000-10-02 00:15:00    45
Freq: 17T, dtype: int64

If needed you can adjust the bins with the argument offset (a Timedelta) that would be added to the default origin.

For a full example, see: Use origin or offset to adjust the start of the bins.

fsspec now used for filesystem handling

For reading and writing to filesystems other than local and reading from HTTP(S), the optional dependency fsspec will be used to dispatch operations (GH33452). This will give unchanged functionality for S3 and GCS storage, which were already supported, but also add support for several other storage implementations such as Azure Datalake and Blob, SSH, FTP, dropbox and github. For docs and capabilities, see the fsspec docs.

The existing capability to interface with S3 and GCS will be unaffected by this change, as fsspec will still bring in the same packages as before.

Other enhancements

Notable bug fixes

These are bug fixes that might have notable behavior changes.

MultiIndex.get_indexer interprets method argument correctly

This restores the behavior of MultiIndex.get_indexer() with method='backfill' or method='pad' to the behavior before pandas 0.23.0. In particular, MultiIndexes are treated as a list of tuples and padding or backfilling is done with respect to the ordering of these lists of tuples (GH29896).

As an example of this, given:

In [48]: df = pd.DataFrame({
   ....:     'a': [0, 0, 0, 0],
   ....:     'b': [0, 2, 3, 4],
   ....:     'c': ['A', 'B', 'C', 'D'],
   ....: }).set_index(['a', 'b'])
   ....: 

In [49]: mi_2 = pd.MultiIndex.from_product([[0], [-1, 0, 1, 3, 4, 5]])

The differences in reindexing df with mi_2 and using method='backfill' can be seen here:

pandas >= 0.23, < 1.1.0:

In [1]: df.reindex(mi_2, method='backfill')
Out[1]:
      c
0 -1  A
   0  A
   1  D
   3  A
   4  A
   5  C

pandas <0.23, >= 1.1.0

In [50]: df.reindex(mi_2, method='backfill')
Out[50]: 
        c
0 -1    A
   0    A
   1    B
   3    C
   4    D
   5  NaN

And the differences in reindexing df with mi_2 and using method='pad' can be seen here:

pandas >= 0.23, < 1.1.0

In [1]: df.reindex(mi_2, method='pad')
Out[1]:
        c
0 -1  NaN
   0  NaN
   1    D
   3  NaN
   4    A
   5    C

pandas < 0.23, >= 1.1.0

In [51]: df.reindex(mi_2, method='pad')
Out[51]: 
        c
0 -1  NaN
   0    A
   1    A
   3    C
   4    D
   5    D

Failed label-based lookups always raise KeyError

Label lookups series[key], series.loc[key] and frame.loc[key] used to raise either KeyError or TypeError depending on the type of key and type of Index. These now consistently raise KeyError (GH31867)

In [52]: ser1 = pd.Series(range(3), index=[0, 1, 2])

In [53]: ser2 = pd.Series(range(3), index=pd.date_range("2020-02-01", periods=3))

Previous behavior:

In [3]: ser1[1.5]
...
TypeError: cannot do label indexing on Int64Index with these indexers [1.5] of type float

In [4] ser1["foo"]
...
KeyError: 'foo'

In [5]: ser1.loc[1.5]
...
TypeError: cannot do label indexing on Int64Index with these indexers [1.5] of type float

In [6]: ser1.loc["foo"]
...
KeyError: 'foo'

In [7]: ser2.loc[1]
...
TypeError: cannot do label indexing on DatetimeIndex with these indexers [1] of type int

In [8]: ser2.loc[pd.Timestamp(0)]
...
KeyError: Timestamp('1970-01-01 00:00:00')

New behavior:

In [3]: ser1[1.5]
...
KeyError: 1.5

In [4] ser1["foo"]
...
KeyError: 'foo'

In [5]: ser1.loc[1.5]
...
KeyError: 1.5

In [6]: ser1.loc["foo"]
...
KeyError: 'foo'

In [7]: ser2.loc[1]
...
KeyError: 1

In [8]: ser2.loc[pd.Timestamp(0)]
...
KeyError: Timestamp('1970-01-01 00:00:00')

Similarly, DataFrame.at() and Series.at() will raise a TypeError instead of a ValueError if an incompatible key is passed, and KeyError if a missing key is passed, matching the behavior of .loc[] (GH31722)

Failed Integer Lookups on MultiIndex Raise KeyError

Indexing with integers with a MultiIndex that has an integer-dtype first level incorrectly failed to raise KeyError when one or more of those integer keys is not present in the first level of the index (GH33539)

In [54]: idx = pd.Index(range(4))

In [55]: dti = pd.date_range("2000-01-03", periods=3)

In [56]: mi = pd.MultiIndex.from_product([idx, dti])

In [57]: ser = pd.Series(range(len(mi)), index=mi)

Previous behavior:

In [5]: ser[[5]]
Out[5]: Series([], dtype: int64)

New behavior:

In [5]: ser[[5]]
...
KeyError: '[5] not in index'

DataFrame.merge() preserves right frame’s row order

DataFrame.merge() now preserves the right frame’s row order when executing a right merge (GH27453)

In [58]: left_df = pd.DataFrame({'animal': ['dog', 'pig'],
   ....:                        'max_speed': [40, 11]})
   ....: 

In [59]: right_df = pd.DataFrame({'animal': ['quetzal', 'pig'],
   ....:                         'max_speed': [80, 11]})
   ....: 

In [60]: left_df
Out[60]: 
  animal  max_speed
0    dog         40
1    pig         11

In [61]: right_df
Out[61]: 
    animal  max_speed
0  quetzal         80
1      pig         11

Previous behavior:

>>> left_df.merge(right_df, on=['animal', 'max_speed'], how="right")
    animal  max_speed
0      pig         11
1  quetzal         80

New behavior:

In [62]: left_df.merge(right_df, on=['animal', 'max_speed'], how="right")
Out[62]: 
    animal  max_speed
0  quetzal         80
1      pig         11

Assignment to multiple columns of a DataFrame when some columns do not exist

Assignment to multiple columns of a DataFrame when some of the columns do not exist would previously assign the values to the last column. Now, new columns will be constructed with the right values. (GH13658)

In [63]: df = pd.DataFrame({'a': [0, 1, 2], 'b': [3, 4, 5]})

In [64]: df
Out[64]: 
   a  b
0  0  3
1  1  4
2  2  5

Previous behavior:

In [3]: df[['a', 'c']] = 1
In [4]: df
Out[4]:
   a  b
0  1  1
1  1  1
2  1  1

New behavior:

In [65]: df[['a', 'c']] = 1

In [66]: df
Out[66]: 
   a  b  c
0  1  3  1
1  1  4  1
2  1  5  1

Consistency across groupby reductions

Using DataFrame.groupby() with as_index=True and the aggregation nunique would include the grouping column(s) in the columns of the result. Now the grouping column(s) only appear in the index, consistent with other reductions. (GH32579)

In [67]: df = pd.DataFrame({"a": ["x", "x", "y", "y"], "b": [1, 1, 2, 3]})

In [68]: df
Out[68]: 
   a  b
0  x  1
1  x  1
2  y  2
3  y  3

Previous behavior:

In [3]: df.groupby("a", as_index=True).nunique()
Out[4]:
   a  b
a
x  1  1
y  1  2

New behavior:

In [69]: df.groupby("a", as_index=True).nunique()
Out[69]: 
   b
a   
x  1
y  2

Using DataFrame.groupby() with as_index=False and the function idxmax, idxmin, mad, nunique, sem, skew, or std would modify the grouping column. Now the grouping column remains unchanged, consistent with other reductions. (GH21090, GH10355)

Previous behavior:

In [3]: df.groupby("a", as_index=False).nunique()
Out[4]:
   a  b
0  1  1
1  1  2

New behavior:

In [70]: df.groupby("a", as_index=False).nunique()
Out[70]: 
   a  b
0  x  1
1  y  2

The method size() would previously ignore as_index=False. Now the grouping columns are returned as columns, making the result a DataFrame instead of a Series. (GH32599)

Previous behavior:

In [3]: df.groupby("a", as_index=False).size()
Out[4]:
a
x    2
y    2
dtype: int64

New behavior:

In [71]: df.groupby("a", as_index=False).size()
Out[71]: 
   a  size
0  x     2
1  y     2

agg() lost results with as_index=False when relabeling columns

Previously agg() lost the result columns, when the as_index option was set to False and the result columns were relabeled. In this case the result values were replaced with the previous index (GH32240).

In [72]: df = pd.DataFrame({"key": ["x", "y", "z", "x", "y", "z"],
   ....:                    "val": [1.0, 0.8, 2.0, 3.0, 3.6, 0.75]})
   ....: 

In [73]: df
Out[73]: 
  key   val
0   x  1.00
1   y  0.80
2   z  2.00
3   x  3.00
4   y  3.60
5   z  0.75

Previous behavior:

In [2]: grouped = df.groupby("key", as_index=False)
In [3]: result = grouped.agg(min_val=pd.NamedAgg(column="val", aggfunc="min"))
In [4]: result
Out[4]:
     min_val
 0   x
 1   y
 2   z

New behavior:

In [74]: grouped = df.groupby("key", as_index=False)

In [75]: result = grouped.agg(min_val=pd.NamedAgg(column="val", aggfunc="min"))

In [76]: result
Out[76]: 
  key  min_val
0   x     1.00
1   y     0.80
2   z     0.75

apply and applymap on DataFrame evaluates first row/column only once

In [77]: df = pd.DataFrame({'a': [1, 2], 'b': [3, 6]})

In [78]: def func(row):
   ....:     print(row)
   ....:     return row
   ....:

Previous behavior:

In [4]: df.apply(func, axis=1)
a    1
b    3
Name: 0, dtype: int64
a    1
b    3
Name: 0, dtype: int64
a    2
b    6
Name: 1, dtype: int64
Out[4]:
   a  b
0  1  3
1  2  6

New behavior:

In [79]: df.apply(func, axis=1)
a    1
b    3
Name: 0, dtype: int64
a    2
b    6
Name: 1, dtype: int64
Out[79]: 
   a  b
0  1  3
1  2  6

Backwards incompatible API changes

Added check_freq argument to testing.assert_frame_equal and testing.assert_series_equal

The check_freq argument was added to testing.assert_frame_equal() and testing.assert_series_equal() in pandas 1.1.0 and defaults to True. testing.assert_frame_equal() and testing.assert_series_equal() now raise AssertionError if the indexes do not have the same frequency. Before pandas 1.1.0, the index frequency was not checked.

Increased minimum versions for dependencies

Some minimum supported versions of dependencies were updated (GH33718, GH29766, GH29723, pytables >= 3.4.3). If installed, we now require:

Package

Minimum Version

Required

Changed

numpy

1.15.4

X

X

pytz

2015.4

X

python-dateutil

2.7.3

X

X

bottleneck

1.2.1

numexpr

2.6.2

pytest (dev)

4.0.2

For optional libraries the general recommendation is to use the latest version. The following table lists the lowest version per library that is currently being tested throughout the development of pandas. Optional libraries below the lowest tested version may still work, but are not considered supported.

Package

Minimum Version

Changed

beautifulsoup4

4.6.0

fastparquet

0.3.2

fsspec

0.7.4

gcsfs

0.6.0

X

lxml

3.8.0

matplotlib

2.2.2

numba

0.46.0

openpyxl

2.5.7

pyarrow

0.13.0

pymysql

0.7.1

pytables

3.4.3

X

s3fs

0.4.0

X

scipy

1.2.0

X

sqlalchemy

1.1.4

xarray

0.8.2

xlrd

1.1.0

xlsxwriter

0.9.8

xlwt

1.2.0

pandas-gbq

1.2.0

X

See Dependencies and Optional dependencies for more.

Development changes

  • The minimum version of Cython is now the most recent bug-fix version (0.29.16) (GH33334).

Deprecations

  • Lookups on a Series with a single-item list containing a slice (e.g. ser[[slice(0, 4)]]) are deprecated and will raise in a future version. Either convert the list to a tuple, or pass the slice directly instead (GH31333)

  • DataFrame.mean() and DataFrame.median() with numeric_only=None will include datetime64 and datetime64tz columns in a future version (GH29941)

  • Setting values with .loc using a positional slice is deprecated and will raise in a future version. Use .loc with labels or .iloc with positions instead (GH31840)

  • DataFrame.to_dict() has deprecated accepting short names for orient and will raise in a future version (GH32515)

  • Categorical.to_dense() is deprecated and will be removed in a future version, use np.asarray(cat) instead (GH32639)

  • The fastpath keyword in the SingleBlockManager constructor is deprecated and will be removed in a future version (GH33092)

  • Providing suffixes as a set in pandas.merge() is deprecated. Provide a tuple instead (GH33740, GH34741).

  • Indexing a Series with a multi-dimensional indexer like [:, None] to return an ndarray now raises a FutureWarning. Convert to a NumPy array before indexing instead (GH27837)

  • Index.is_mixed() is deprecated and will be removed in a future version, check index.inferred_type directly instead (GH32922)

  • Passing any arguments but the first one to read_html() as positional arguments is deprecated. All other arguments should be given as keyword arguments (GH27573).

  • Passing any arguments but path_or_buf (the first one) to read_json() as positional arguments is deprecated. All other arguments should be given as keyword arguments (GH27573).

  • Passing any arguments but the first two to read_excel() as positional arguments is deprecated. All other arguments should be given as keyword arguments (GH27573).

  • pandas.api.types.is_categorical() is deprecated and will be removed in a future version; use pandas.api.types.is_categorical_dtype() instead (GH33385)

  • Index.get_value() is deprecated and will be removed in a future version (GH19728)

  • Series.dt.week() and Series.dt.weekofyear() are deprecated and will be removed in a future version, use Series.dt.isocalendar().week() instead (GH33595)

  • DatetimeIndex.week() and DatetimeIndex.weekofyear are deprecated and will be removed in a future version, use DatetimeIndex.isocalendar().week instead (GH33595)

  • DatetimeArray.week() and DatetimeArray.weekofyear are deprecated and will be removed in a future version, use DatetimeArray.isocalendar().week instead (GH33595)

  • DateOffset.__call__() is deprecated and will be removed in a future version, use offset + other instead (GH34171)

  • apply_index() is deprecated and will be removed in a future version. Use offset + other instead (GH34580)

  • DataFrame.tshift() and Series.tshift() are deprecated and will be removed in a future version, use DataFrame.shift() and Series.shift() instead (GH11631)

  • Indexing an Index object with a float key is deprecated, and will raise an IndexError in the future. You can manually convert to an integer key instead (GH34191).

  • The squeeze keyword in groupby() is deprecated and will be removed in a future version (GH32380)

  • The tz keyword in Period.to_timestamp() is deprecated and will be removed in a future version; use per.to_timestamp(...).tz_localize(tz) instead (GH34522)

  • DatetimeIndex.to_perioddelta() is deprecated and will be removed in a future version. Use index - index.to_period(freq).to_timestamp() instead (GH34853)

  • DataFrame.melt() accepting a value_name that already exists is deprecated, and will be removed in a future version (GH34731)

  • The center keyword in the DataFrame.expanding() function is deprecated and will be removed in a future version (GH20647)

Performance improvements

Bug fixes

Categorical

  • Passing an invalid fill_value to Categorical.take() raises a ValueError instead of TypeError (GH33660)

  • Combining a Categorical with integer categories and which contains missing values with a float dtype column in operations such as concat() or append() will now result in a float column instead of an object dtype column (GH33607)

  • Bug where merge() was unable to join on non-unique categorical indices (GH28189)

  • Bug when passing categorical data to Index constructor along with dtype=object incorrectly returning a CategoricalIndex instead of object-dtype Index (GH32167)

  • Bug where Categorical comparison operator __ne__ would incorrectly evaluate to False when either element was missing (GH32276)

  • Categorical.fillna() now accepts Categorical other argument (GH32420)

  • Repr of Categorical was not distinguishing between int and str (GH33676)

Datetimelike

  • Passing an integer dtype other than int64 to np.array(period_index, dtype=...) will now raise TypeError instead of incorrectly using int64 (GH32255)

  • Series.to_timestamp() now raises a TypeError if the axis is not a PeriodIndex. Previously an AttributeError was raised (GH33327)

  • Series.to_period() now raises a TypeError if the axis is not a DatetimeIndex. Previously an AttributeError was raised (GH33327)

  • Period no longer accepts tuples for the freq argument (GH34658)

  • Bug in Timestamp where constructing a Timestamp from ambiguous epoch time and calling constructor again changed the Timestamp.value() property (GH24329)

  • DatetimeArray.searchsorted(), TimedeltaArray.searchsorted(), PeriodArray.searchsorted() not recognizing non-pandas scalars and incorrectly raising ValueError instead of TypeError (GH30950)

  • Bug in Timestamp where constructing Timestamp with dateutil timezone less than 128 nanoseconds before daylight saving time switch from winter to summer would result in nonexistent time (GH31043)

  • Bug in Period.to_timestamp(), Period.start_time() with microsecond frequency returning a timestamp one nanosecond earlier than the correct time (GH31475)

  • Timestamp raised a confusing error message when year, month or day is missing (GH31200)

  • Bug in DatetimeIndex constructor incorrectly accepting bool-dtype inputs (GH32668)

  • Bug in DatetimeIndex.searchsorted() not accepting a list or Series as its argument (GH32762)

  • Bug where PeriodIndex() raised when passed a Series of strings (GH26109)

  • Bug in Timestamp arithmetic when adding or subtracting an np.ndarray with timedelta64 dtype (GH33296)

  • Bug in DatetimeIndex.to_period() not inferring the frequency when called with no arguments (GH33358)

  • Bug in DatetimeIndex.tz_localize() incorrectly retaining freq in some cases where the original freq is no longer valid (GH30511)

  • Bug in DatetimeIndex.intersection() losing freq and timezone in some cases (GH33604)

  • Bug in DatetimeIndex.get_indexer() where incorrect output would be returned for mixed datetime-like targets (GH33741)

  • Bug in DatetimeIndex addition and subtraction with some types of DateOffset objects incorrectly retaining an invalid freq attribute (GH33779)

  • Bug in DatetimeIndex where setting the freq attribute on an index could silently change the freq attribute on another index viewing the same data (GH33552)

  • DataFrame.min() and DataFrame.max() were not returning consistent results with Series.min() and Series.max() when called on objects initialized with empty pd.to_datetime()

  • Bug in DatetimeIndex.intersection() and TimedeltaIndex.intersection() with results not having the correct name attribute (GH33904)

  • Bug in DatetimeArray.__setitem__(), TimedeltaArray.__setitem__(), PeriodArray.__setitem__() incorrectly allowing values with int64 dtype to be silently cast (GH33717)

  • Bug in subtracting TimedeltaIndex from Period incorrectly raising TypeError in some cases where it should succeed and IncompatibleFrequency in some cases where it should raise TypeError (GH33883)

  • Bug in constructing a Series or Index from a read-only NumPy array with non-ns resolution which converted to object dtype instead of coercing to datetime64[ns] dtype when within the timestamp bounds (GH34843).

  • The freq keyword in Period, date_range(), period_range(), pd.tseries.frequencies.to_offset() no longer allows tuples, pass as string instead (GH34703)

  • Bug in DataFrame.append() when appending a Series containing a scalar tz-aware Timestamp to an empty DataFrame resulted in an object column instead of datetime64[ns, tz] dtype (GH35038)

  • OutOfBoundsDatetime issues an improved error message when timestamp is out of implementation bounds. (GH32967)

  • Bug in AbstractHolidayCalendar.holidays() when no rules were defined (GH31415)

  • Bug in Tick comparisons raising TypeError when comparing against timedelta-like objects (GH34088)

  • Bug in Tick multiplication raising TypeError when multiplying by a float (GH34486)

Timedelta

Timezones

  • Bug in to_datetime() with infer_datetime_format=True where timezone names (e.g. UTC) would not be parsed correctly (GH33133)

Numeric

Conversion

  • Bug in Series construction from NumPy array with big-endian datetime64 dtype (GH29684)

  • Bug in Timedelta construction with large nanoseconds keyword value (GH32402)

  • Bug in DataFrame construction where sets would be duplicated rather than raising (GH32582)

  • The DataFrame constructor no longer accepts a list of DataFrame objects. Because of changes to NumPy, DataFrame objects are now consistently treated as 2D objects, so a list of DataFrame objects is considered 3D, and no longer acceptable for the DataFrame constructor (GH32289).

  • Bug in DataFrame when initiating a frame with lists and assign columns with nested list for MultiIndex (GH32173)

  • Improved error message for invalid construction of list when creating a new index (GH35190)

Strings

  • Bug in the astype() method when converting “string” dtype data to nullable integer dtype (GH32450).

  • Fixed issue where taking min or max of a StringArray or Series with StringDtype type would raise. (GH31746)

  • Bug in Series.str.cat() returning NaN output when other had Index type (GH33425)

  • pandas.api.dtypes.is_string_dtype() no longer incorrectly identifies categorical series as string.

Interval

  • Bug in IntervalArray incorrectly allowing the underlying data to be changed when setting values (GH32782)

Indexing

Missing

  • Calling fillna() on an empty Series now correctly returns a shallow copied object. The behaviour is now consistent with Index, DataFrame and a non-empty Series (GH32543).

  • Bug in Series.replace() when argument to_replace is of type dict/list and is used on a Series containing <NA> was raising a TypeError. The method now handles this by ignoring <NA> values when doing the comparison for the replacement (GH32621)

  • Bug in any() and all() incorrectly returning <NA> for all False or all True values using the nulllable Boolean dtype and with skipna=False (GH33253)

  • Clarified documentation on interpolate with method=akima. The der parameter must be scalar or None (GH33426)

  • DataFrame.interpolate() uses the correct axis convention now. Previously interpolating along columns lead to interpolation along indices and vice versa. Furthermore interpolating with methods pad, ffill, bfill and backfill are identical to using these methods with DataFrame.fillna() (GH12918, GH29146)

  • Bug in DataFrame.interpolate() when called on a DataFrame with column names of string type was throwing a ValueError. The method is now independent of the type of the column names (GH33956)

  • Passing NA into a format string using format specs will now work. For example "{:.1f}".format(pd.NA) would previously raise a ValueError, but will now return the string "<NA>" (GH34740)

  • Bug in Series.map() not raising on invalid na_action (GH32815)

MultiIndex

  • DataFrame.swaplevels() now raises a TypeError if the axis is not a MultiIndex. Previously an AttributeError was raised (GH31126)

  • Bug in Dataframe.loc() when used with a MultiIndex. The returned values were not in the same order as the given inputs (GH22797)

In [80]: df = pd.DataFrame(np.arange(4),
   ....:                   index=[["a", "a", "b", "b"], [1, 2, 1, 2]])
   ....: 

# Rows are now ordered as the requested keys
In [81]: df.loc[(['b', 'a'], [2, 1]), :]
Out[81]: 
     0
b 2  3
  1  2
a 2  1
  1  0

In [82]: left = pd.MultiIndex.from_arrays([["b", "a"], [2, 1]])

In [83]: right = pd.MultiIndex.from_arrays([["a", "b", "c"], [1, 2, 3]])

# Common elements are now guaranteed to be ordered by the left side
In [84]: left.intersection(right, sort=False)
Out[84]: 
MultiIndex([('b', 2),
            ('a', 1)],
           )

  • Bug when joining two MultiIndex without specifying level with different columns. Return-indexers parameter was ignored. (GH34074)

IO

  • Passing a set as names argument to pandas.read_csv(), pandas.read_table(), or pandas.read_fwf() will raise ValueError: Names should be an ordered collection. (GH34946)

  • Bug in print-out when display.precision is zero. (GH20359)

  • Bug in read_json() where integer overflow was occurring when json contains big number strings. (GH30320)

  • read_csv() will now raise a ValueError when the arguments header and prefix both are not None. (GH27394)

  • Bug in DataFrame.to_json() was raising NotFoundError when path_or_buf was an S3 URI (GH28375)

  • Bug in DataFrame.to_parquet() overwriting pyarrow’s default for coerce_timestamps; following pyarrow’s default allows writing nanosecond timestamps with version="2.0" (GH31652).

  • Bug in read_csv() was raising TypeError when sep=None was used in combination with comment keyword (GH31396)

  • Bug in HDFStore that caused it to set to int64 the dtype of a datetime64 column when reading a DataFrame in Python 3 from fixed format written in Python 2 (GH31750)

  • read_sas() now handles dates and datetimes larger than Timestamp.max returning them as datetime.datetime objects (GH20927)

  • Bug in DataFrame.to_json() where Timedelta objects would not be serialized correctly with date_format="iso" (GH28256)

  • read_csv() will raise a ValueError when the column names passed in parse_dates are missing in the Dataframe (GH31251)

  • Bug in read_excel() where a UTF-8 string with a high surrogate would cause a segmentation violation (GH23809)

  • Bug in read_csv() was causing a file descriptor leak on an empty file (GH31488)

  • Bug in read_csv() was causing a segfault when there were blank lines between the header and data rows (GH28071)

  • Bug in read_csv() was raising a misleading exception on a permissions issue (GH23784)

  • Bug in read_csv() was raising an IndexError when header=None and two extra data columns

  • Bug in read_sas() was raising an AttributeError when reading files from Google Cloud Storage (GH33069)

  • Bug in DataFrame.to_sql() where an AttributeError was raised when saving an out of bounds date (GH26761)

  • Bug in read_excel() did not correctly handle multiple embedded spaces in OpenDocument text cells. (GH32207)

  • Bug in read_json() was raising TypeError when reading a list of Booleans into a Series. (GH31464)

  • Bug in pandas.io.json.json_normalize() where location specified by record_path doesn’t point to an array. (GH26284)

  • pandas.read_hdf() has a more explicit error message when loading an unsupported HDF file (GH9539)

  • Bug in read_feather() was raising an ArrowIOError when reading an s3 or http file path (GH29055)

  • Bug in to_excel() could not handle the column name render and was raising an KeyError (GH34331)

  • Bug in execute() was raising a ProgrammingError for some DB-API drivers when the SQL statement contained the % character and no parameters were present (GH34211)

  • Bug in StataReader() which resulted in categorical variables with different dtypes when reading data using an iterator. (GH31544)

  • HDFStore.keys() has now an optional include parameter that allows the retrieval of all native HDF5 table names (GH29916)

  • TypeError exceptions raised by read_csv() and read_table() were showing as parser_f when an unexpected keyword argument was passed (GH25648)

  • Bug in read_excel() for ODS files removes 0.0 values (GH27222)

  • Bug in ujson.encode() was raising an OverflowError with numbers larger than sys.maxsize (GH34395)

  • Bug in HDFStore.append_to_multiple() was raising a ValueError when the min_itemsize parameter is set (GH11238)

  • Bug in create_table() now raises an error when column argument was not specified in data_columns on input (GH28156)

  • read_json() now could read line-delimited json file from a file url while lines and chunksize are set.

  • Bug in DataFrame.to_sql() when reading DataFrames with -np.inf entries with MySQL now has a more explicit ValueError (GH34431)

  • Bug where capitalised files extensions were not decompressed by read_* functions (GH35164)

  • Bug in read_excel() that was raising a TypeError when header=None and index_col is given as a list (GH31783)

  • Bug in read_excel() where datetime values are used in the header in a MultiIndex (GH34748)

  • read_excel() no longer takes **kwds arguments. This means that passing in the keyword argument chunksize now raises a TypeError (previously raised a NotImplementedError), while passing in the keyword argument encoding now raises a TypeError (GH34464)

  • Bug in DataFrame.to_records() was incorrectly losing timezone information in timezone-aware datetime64 columns (GH32535)

Plotting

GroupBy/resample/rolling

  • Using a pandas.api.indexers.BaseIndexer with count, min, max, median, skew, cov, corr will now return correct results for any monotonic pandas.api.indexers.BaseIndexer descendant (GH32865)

  • DataFrameGroupby.mean() and SeriesGroupby.mean() (and similarly for median(), std() and var()) now raise a TypeError if a non-accepted keyword argument is passed into it. Previously an UnsupportedFunctionCall was raised (AssertionError if min_count passed into median()) (GH31485)

  • Bug in GroupBy.apply() raises ValueError when the by axis is not sorted, has duplicates, and the applied func does not mutate passed in objects (GH30667)

  • Bug in DataFrameGroupBy.transform() produces an incorrect result with transformation functions (GH30918)

  • Bug in Groupby.transform() was returning the wrong result when grouping by multiple keys of which some were categorical and others not (GH32494)

  • Bug in GroupBy.count() causes segmentation fault when grouped-by columns contain NaNs (GH32841)

  • Bug in DataFrame.groupby() and Series.groupby() produces inconsistent type when aggregating Boolean Series (GH32894)

  • Bug in DataFrameGroupBy.sum() and SeriesGroupBy.sum() where a large negative number would be returned when the number of non-null values was below min_count for nullable integer dtypes (GH32861)

  • Bug in SeriesGroupBy.quantile() was raising on nullable integers (GH33136)

  • Bug in DataFrame.resample() where an AmbiguousTimeError would be raised when the resulting timezone aware DatetimeIndex had a DST transition at midnight (GH25758)

  • Bug in DataFrame.groupby() where a ValueError would be raised when grouping by a categorical column with read-only categories and sort=False (GH33410)

  • Bug in GroupBy.agg(), GroupBy.transform(), and GroupBy.resample() where subclasses are not preserved (GH28330)

  • Bug in SeriesGroupBy.agg() where any column name was accepted in the named aggregation of SeriesGroupBy previously. The behaviour now allows only str and callables else would raise TypeError. (GH34422)

  • Bug in DataFrame.groupby() lost the name of the Index when one of the agg keys referenced an empty list (GH32580)

  • Bug in Rolling.apply() where center=True was ignored when engine='numba' was specified (GH34784)

  • Bug in DataFrame.ewm.cov() was throwing AssertionError for MultiIndex inputs (GH34440)

  • Bug in core.groupby.DataFrameGroupBy.quantile() raised TypeError for non-numeric types rather than dropping the columns (GH27892)

  • Bug in core.groupby.DataFrameGroupBy.transform() when func='nunique' and columns are of type datetime64, the result would also be of type datetime64 instead of int64 (GH35109)

  • Bug in DataFrame.groupby() raising an AttributeError when selecting a column and aggregating with as_index=False (GH35246).

  • Bug in DataFrameGroupBy.first() and DataFrameGroupBy.last() that would raise an unnecessary ValueError when grouping on multiple Categoricals (GH34951)

Reshaping

Sparse

  • Creating a SparseArray from timezone-aware dtype will issue a warning before dropping timezone information, instead of doing so silently (GH32501)

  • Bug in arrays.SparseArray.from_spmatrix() wrongly read scipy sparse matrix (GH31991)

  • Bug in Series.sum() with SparseArray raised a TypeError (GH25777)

  • Bug where DataFrame containing an all-sparse SparseArray filled with NaN when indexed by a list-like (GH27781, GH29563)

  • The repr of SparseDtype now includes the repr of its fill_value attribute. Previously it used fill_value’s string representation (GH34352)

  • Bug where empty DataFrame could not be cast to SparseDtype (GH33113)

  • Bug in arrays.SparseArray() was returning the incorrect type when indexing a sparse dataframe with an iterable (GH34526, GH34540)

ExtensionArray

  • Fixed bug where Series.value_counts() would raise on empty input of Int64 dtype (GH33317)

  • Fixed bug in concat() when concatenating DataFrame objects with non-overlapping columns resulting in object-dtype columns rather than preserving the extension dtype (GH27692, GH33027)

  • Fixed bug where StringArray.isna() would return False for NA values when pandas.options.mode.use_inf_as_na was set to True (GH33655)

  • Fixed bug in Series construction with EA dtype and index but no data or scalar data fails (GH26469)

  • Fixed bug that caused Series.__repr__() to crash for extension types whose elements are multidimensional arrays (GH33770).

  • Fixed bug where Series.update() would raise a ValueError for ExtensionArray dtypes with missing values (GH33980)

  • Fixed bug where StringArray.memory_usage() was not implemented (GH33963)

  • Fixed bug where DataFrameGroupBy() would ignore the min_count argument for aggregations on nullable Boolean dtypes (GH34051)

  • Fixed bug where the constructor of DataFrame with dtype='string' would fail (GH27953, GH33623)

  • Bug where DataFrame column set to scalar extension type was considered an object type rather than the extension type (GH34832)

  • Fixed bug in IntegerArray.astype() to correctly copy the mask as well (GH34931).

Other

Contributors

A total of 368 people contributed patches to this release. People with a “+” by their names contributed a patch for the first time.

  • 3vts +

  • A Brooks +

  • Abbie Popa +

  • Achmad Syarif Hidayatullah +

  • Adam W Bagaskarta +

  • Adrian Mastronardi +

  • Aidan Montare +

  • Akbar Septriyan +

  • Akos Furton +

  • Alejandro Hall +

  • Alex Hall +

  • Alex Itkes +

  • Alex Kirko

  • Ali McMaster +

  • Alvaro Aleman +

  • Amy Graham +

  • Andrew Schonfeld +

  • Andrew Shumanskiy +

  • Andrew Wieteska +

  • Angela Ambroz

  • Anjali Singh +

  • Anna Daglis

  • Anthony Milbourne +

  • Antony Lee +

  • Ari Sosnovsky +

  • Arkadeep Adhikari +

  • Arunim Samudra +

  • Ashkan +

  • Ashwin Prakash Nalwade +

  • Ashwin Srinath +

  • Atsushi Nukariya +

  • Ayappan +

  • Ayla Khan +

  • Bart +

  • Bart Broere +

  • Benjamin Beier Liu +

  • Benjamin Fischer +

  • Bharat Raghunathan

  • Bradley Dice +

  • Brendan Sullivan +

  • Brian Strand +

  • Carsten van Weelden +

  • Chamoun Saoma +

  • ChrisRobo +

  • Christian Chwala

  • Christopher Whelan

  • Christos Petropoulos +

  • Chuanzhu Xu

  • CloseChoice +

  • Clément Robert +

  • CuylenE +

  • DanBasson +

  • Daniel Saxton

  • Danilo Horta +

  • DavaIlhamHaeruzaman +

  • Dave Hirschfeld

  • Dave Hughes

  • David Rouquet +

  • David S +

  • Deepyaman Datta

  • Dennis Bakhuis +

  • Derek McCammond +

  • Devjeet Roy +

  • Diane Trout

  • Dina +

  • Dom +

  • Drew Seibert +

  • EdAbati

  • Emiliano Jordan +

  • Erfan Nariman +

  • Eric Groszman +

  • Erik Hasse +

  • Erkam Uyanik +

  • Evan D +

  • Evan Kanter +

  • Fangchen Li +

  • Farhan Reynaldo +

  • Farhan Reynaldo Hutabarat +

  • Florian Jetter +

  • Fred Reiss +

  • GYHHAHA +

  • Gabriel Moreira +

  • Gabriel Tutui +

  • Galuh Sahid

  • Gaurav Chauhan +

  • George Hartzell +

  • Gim Seng +

  • Giovanni Lanzani +

  • Gordon Chen +

  • Graham Wetzler +

  • Guillaume Lemaitre

  • Guillem Sánchez +

  • HH-MWB +

  • Harshavardhan Bachina

  • How Si Wei

  • Ian Eaves

  • Iqrar Agalosi Nureyza +

  • Irv Lustig

  • Iva Laginja +

  • JDkuba

  • Jack Greisman +

  • Jacob Austin +

  • Jacob Deppen +

  • Jacob Peacock +

  • Jake Tae +

  • Jake Vanderplas +

  • James Cobon-Kerr

  • Jan Červenka +

  • Jan Škoda

  • Jane Chen +

  • Jean-Francois Zinque +

  • Jeanderson Barros Candido +

  • Jeff Reback

  • Jered Dominguez-Trujillo +

  • Jeremy Schendel

  • Jesse Farnham

  • Jiaxiang

  • Jihwan Song +

  • Joaquim L. Viegas +

  • Joel Nothman

  • John Bodley +

  • John Paton +

  • Jon Thielen +

  • Joris Van den Bossche

  • Jose Manuel Martí +

  • Joseph Gulian +

  • Josh Dimarsky

  • Joy Bhalla +

  • João Veiga +

  • Julian de Ruiter +

  • Justin Essert +

  • Justin Zheng

  • KD-dev-lab +

  • Kaiqi Dong

  • Karthik Mathur +

  • Kaushal Rohit +

  • Kee Chong Tan

  • Ken Mankoff +

  • Kendall Masse

  • Kenny Huynh +

  • Ketan +

  • Kevin Anderson +

  • Kevin Bowey +

  • Kevin Sheppard

  • Kilian Lieret +

  • Koki Nishihara +

  • Krishna Chivukula +

  • KrishnaSai2020 +

  • Lesley +

  • Lewis Cowles +

  • Linda Chen +

  • Linxiao Wu +

  • Lucca Delchiaro Costabile +

  • MBrouns +

  • Mabel Villalba

  • Mabroor Ahmed +

  • Madhuri Palanivelu +

  • Mak Sze Chun

  • Malcolm +

  • Marc Garcia

  • Marco Gorelli

  • Marian Denes +

  • Martin Bjeldbak Madsen +

  • Martin Durant +

  • Martin Fleischmann +

  • Martin Jones +

  • Martin Winkel

  • Martina Oefelein +

  • Marvzinc +

  • María Marino +

  • Matheus Cardoso +

  • Mathis Felardos +

  • Matt Roeschke

  • Matteo Felici +

  • Matteo Santamaria +

  • Matthew Roeschke

  • Matthias Bussonnier

  • Max Chen

  • Max Halford +

  • Mayank Bisht +

  • Megan Thong +

  • Michael Marino +

  • Miguel Marques +

  • Mike Kutzma

  • Mohammad Hasnain Mohsin Rajan +

  • Mohammad Jafar Mashhadi +

  • MomIsBestFriend

  • Monica +

  • Natalie Jann

  • Nate Armstrong +

  • Nathanael +

  • Nick Newman +

  • Nico Schlömer +

  • Niklas Weber +

  • ObliviousParadigm +

  • Olga Lyashevska +

  • OlivierLuG +

  • Pandas Development Team

  • Parallels +

  • Patrick +

  • Patrick Cando +

  • Paul Lilley +

  • Paul Sanders +

  • Pearcekieser +

  • Pedro Larroy +

  • Pedro Reys

  • Peter Bull +

  • Peter Steinbach +

  • Phan Duc Nhat Minh +

  • Phil Kirlin +

  • Pierre-Yves Bourguignon +

  • Piotr Kasprzyk +

  • Piotr Niełacny +

  • Prakhar Pandey

  • Prashant Anand +

  • Puneetha Pai +

  • Quang Nguyễn +

  • Rafael Jaimes III +

  • Rafif +

  • RaisaDZ +

  • Rakshit Naidu +

  • Ram Rachum +

  • Red +

  • Ricardo Alanis +

  • Richard Shadrach +

  • Rik-de-Kort

  • Robert de Vries

  • Robin to Roxel +

  • Roger Erens +

  • Rohith295 +

  • Roman Yurchak

  • Ror +

  • Rushabh Vasani

  • Ryan

  • Ryan Nazareth

  • SAI SRAVAN MEDICHERLA +

  • SHUBH CHATTERJEE +

  • Sam Cohan

  • Samira-g-js +

  • Sandu Ursu +

  • Sang Agung +

  • SanthoshBala18 +

  • Sasidhar Kasturi +

  • SatheeshKumar Mohan +

  • Saul Shanabrook

  • Scott Gigante +

  • Sebastian Berg +

  • Sebastián Vanrell

  • Sergei Chipiga +

  • Sergey +

  • ShilpaSugan +

  • Simon Gibbons

  • Simon Hawkins

  • Simon Legner +

  • Soham Tiwari +

  • Song Wenhao +

  • Souvik Mandal

  • Spencer Clark

  • Steffen Rehberg +

  • Steffen Schmitz +

  • Stijn Van Hoey

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