In this section, we will discuss missing (also referred to as NA) values in pandas.
Note
The choice of using NaN internally to denote missing data was largely for simplicity and performance reasons. Starting from pandas 1.0, some optional data types start experimenting with a native NA scalar using a mask-based approach. See here for more.
NaN
NA
See the cookbook for some advanced strategies.
As data comes in many shapes and forms, pandas aims to be flexible with regard to handling missing data. While NaN is the default missing value marker for reasons of computational speed and convenience, we need to be able to easily detect this value with data of different types: floating point, integer, boolean, and general object. In many cases, however, the Python None will arise and we wish to also consider that “missing” or “not available” or “NA”.
None
If you want to consider inf and -inf to be “NA” in computations, you can set pandas.options.mode.use_inf_as_na = True.
inf
-inf
pandas.options.mode.use_inf_as_na = True
In [1]: df = pd.DataFrame(np.random.randn(5, 3), index=['a', 'c', 'e', 'f', 'h'], ...: columns=['one', 'two', 'three']) ...: In [2]: df['four'] = 'bar' In [3]: df['five'] = df['one'] > 0 In [4]: df Out[4]: one two three four five a 0.469112 -0.282863 -1.509059 bar True c -1.135632 1.212112 -0.173215 bar False e 0.119209 -1.044236 -0.861849 bar True f -2.104569 -0.494929 1.071804 bar False h 0.721555 -0.706771 -1.039575 bar True In [5]: df2 = df.reindex(['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h']) In [6]: df2 Out[6]: one two three four five a 0.469112 -0.282863 -1.509059 bar True b NaN NaN NaN NaN NaN c -1.135632 1.212112 -0.173215 bar False d NaN NaN NaN NaN NaN e 0.119209 -1.044236 -0.861849 bar True f -2.104569 -0.494929 1.071804 bar False g NaN NaN NaN NaN NaN h 0.721555 -0.706771 -1.039575 bar True
To make detecting missing values easier (and across different array dtypes), pandas provides the isna() and notna() functions, which are also methods on Series and DataFrame objects:
isna()
notna()
In [7]: df2['one'] Out[7]: a 0.469112 b NaN c -1.135632 d NaN e 0.119209 f -2.104569 g NaN h 0.721555 Name: one, dtype: float64 In [8]: pd.isna(df2['one']) Out[8]: a False b True c False d True e False f False g True h False Name: one, dtype: bool In [9]: df2['four'].notna() Out[9]: a True b False c True d False e True f True g False h True Name: four, dtype: bool In [10]: df2.isna() Out[10]: one two three four five a False False False False False b True True True True True c False False False False False d True True True True True e False False False False False f False False False False False g True True True True True h False False False False False
Warning
One has to be mindful that in Python (and NumPy), the nan's don’t compare equal, but None's do. Note that pandas/NumPy uses the fact that np.nan != np.nan, and treats None like np.nan.
nan's
None's
np.nan != np.nan
np.nan
In [11]: None == None # noqa: E711 Out[11]: True In [12]: np.nan == np.nan Out[12]: False
So as compared to above, a scalar equality comparison versus a None/np.nan doesn’t provide useful information.
None/np.nan
In [13]: df2['one'] == np.nan Out[13]: a False b False c False d False e False f False g False h False Name: one, dtype: bool
Because NaN is a float, a column of integers with even one missing values is cast to floating-point dtype (see Support for integer NA for more). Pandas provides a nullable integer array, which can be used by explicitly requesting the dtype:
In [14]: pd.Series([1, 2, np.nan, 4], dtype=pd.Int64Dtype()) Out[14]: 0 1 1 2 2 <NA> 3 4 dtype: Int64
Alternatively, the string alias dtype='Int64' (note the capital "I") can be used.
dtype='Int64'
"I"
See Nullable integer data type for more.
For datetime64[ns] types, NaT represents missing values. This is a pseudo-native sentinel value that can be represented by NumPy in a singular dtype (datetime64[ns]). pandas objects provide compatibility between NaT and NaN.
NaT
In [15]: df2 = df.copy() In [16]: df2['timestamp'] = pd.Timestamp('20120101') In [17]: df2 Out[17]: one two three four five timestamp a 0.469112 -0.282863 -1.509059 bar True 2012-01-01 c -1.135632 1.212112 -0.173215 bar False 2012-01-01 e 0.119209 -1.044236 -0.861849 bar True 2012-01-01 f -2.104569 -0.494929 1.071804 bar False 2012-01-01 h 0.721555 -0.706771 -1.039575 bar True 2012-01-01 In [18]: df2.loc[['a', 'c', 'h'], ['one', 'timestamp']] = np.nan In [19]: df2 Out[19]: one two three four five timestamp a NaN -0.282863 -1.509059 bar True NaT c NaN 1.212112 -0.173215 bar False NaT e 0.119209 -1.044236 -0.861849 bar True 2012-01-01 f -2.104569 -0.494929 1.071804 bar False 2012-01-01 h NaN -0.706771 -1.039575 bar True NaT In [20]: df2.dtypes.value_counts() Out[20]: float64 3 datetime64[ns] 1 bool 1 object 1 dtype: int64
You can insert missing values by simply assigning to containers. The actual missing value used will be chosen based on the dtype.
For example, numeric containers will always use NaN regardless of the missing value type chosen:
In [21]: s = pd.Series([1, 2, 3]) In [22]: s.loc[0] = None In [23]: s Out[23]: 0 NaN 1 2.0 2 3.0 dtype: float64
Likewise, datetime containers will always use NaT.
For object containers, pandas will use the value given:
In [24]: s = pd.Series(["a", "b", "c"]) In [25]: s.loc[0] = None In [26]: s.loc[1] = np.nan In [27]: s Out[27]: 0 None 1 NaN 2 c dtype: object
Missing values propagate naturally through arithmetic operations between pandas objects.
In [28]: a Out[28]: one two a NaN -0.282863 c NaN 1.212112 e 0.119209 -1.044236 f -2.104569 -0.494929 h -2.104569 -0.706771 In [29]: b Out[29]: one two three a NaN -0.282863 -1.509059 c NaN 1.212112 -0.173215 e 0.119209 -1.044236 -0.861849 f -2.104569 -0.494929 1.071804 h NaN -0.706771 -1.039575 In [30]: a + b Out[30]: one three two a NaN NaN -0.565727 c NaN NaN 2.424224 e 0.238417 NaN -2.088472 f -4.209138 NaN -0.989859 h NaN NaN -1.413542
The descriptive statistics and computational methods discussed in the data structure overview (and listed here and here) are all written to account for missing data. For example:
When summing data, NA (missing) values will be treated as zero.
If the data are all NA, the result will be 0.
Cumulative methods like cumsum() and cumprod() ignore NA values by default, but preserve them in the resulting arrays. To override this behaviour and include NA values, use skipna=False.
cumsum()
cumprod()
skipna=False
In [31]: df Out[31]: one two three a NaN -0.282863 -1.509059 c NaN 1.212112 -0.173215 e 0.119209 -1.044236 -0.861849 f -2.104569 -0.494929 1.071804 h NaN -0.706771 -1.039575 In [32]: df['one'].sum() Out[32]: -1.9853605075978744 In [33]: df.mean(1) Out[33]: a -0.895961 c 0.519449 e -0.595625 f -0.509232 h -0.873173 dtype: float64 In [34]: df.cumsum() Out[34]: one two three a NaN -0.282863 -1.509059 c NaN 0.929249 -1.682273 e 0.119209 -0.114987 -2.544122 f -1.985361 -0.609917 -1.472318 h NaN -1.316688 -2.511893 In [35]: df.cumsum(skipna=False) Out[35]: one two three a NaN -0.282863 -1.509059 c NaN 0.929249 -1.682273 e NaN -0.114987 -2.544122 f NaN -0.609917 -1.472318 h NaN -1.316688 -2.511893
This behavior is now standard as of v0.22.0 and is consistent with the default in numpy; previously sum/prod of all-NA or empty Series/DataFrames would return NaN. See v0.22.0 whatsnew for more.
numpy
The sum of an empty or all-NA Series or column of a DataFrame is 0.
In [36]: pd.Series([np.nan]).sum() Out[36]: 0.0 In [37]: pd.Series([], dtype="float64").sum() Out[37]: 0.0
The product of an empty or all-NA Series or column of a DataFrame is 1.
In [38]: pd.Series([np.nan]).prod() Out[38]: 1.0 In [39]: pd.Series([], dtype="float64").prod() Out[39]: 1.0
NA groups in GroupBy are automatically excluded. This behavior is consistent with R, for example:
In [40]: df Out[40]: one two three a NaN -0.282863 -1.509059 c NaN 1.212112 -0.173215 e 0.119209 -1.044236 -0.861849 f -2.104569 -0.494929 1.071804 h NaN -0.706771 -1.039575 In [41]: df.groupby('one').mean() Out[41]: two three one -2.104569 -0.494929 1.071804 0.119209 -1.044236 -0.861849
See the groupby section here for more information.
pandas objects are equipped with various data manipulation methods for dealing with missing data.
fillna() can “fill in” NA values with non-NA data in a couple of ways, which we illustrate:
fillna()
Replace NA with a scalar value
In [42]: df2 Out[42]: one two three four five timestamp a NaN -0.282863 -1.509059 bar True NaT c NaN 1.212112 -0.173215 bar False NaT e 0.119209 -1.044236 -0.861849 bar True 2012-01-01 f -2.104569 -0.494929 1.071804 bar False 2012-01-01 h NaN -0.706771 -1.039575 bar True NaT In [43]: df2.fillna(0) Out[43]: one two three four five timestamp a 0.000000 -0.282863 -1.509059 bar True 0 c 0.000000 1.212112 -0.173215 bar False 0 e 0.119209 -1.044236 -0.861849 bar True 2012-01-01 00:00:00 f -2.104569 -0.494929 1.071804 bar False 2012-01-01 00:00:00 h 0.000000 -0.706771 -1.039575 bar True 0 In [44]: df2['one'].fillna('missing') Out[44]: a missing c missing e 0.119209 f -2.10457 h missing Name: one, dtype: object
Fill gaps forward or backward
Using the same filling arguments as reindexing, we can propagate non-NA values forward or backward:
In [45]: df Out[45]: one two three a NaN -0.282863 -1.509059 c NaN 1.212112 -0.173215 e 0.119209 -1.044236 -0.861849 f -2.104569 -0.494929 1.071804 h NaN -0.706771 -1.039575 In [46]: df.fillna(method='pad') Out[46]: one two three a NaN -0.282863 -1.509059 c NaN 1.212112 -0.173215 e 0.119209 -1.044236 -0.861849 f -2.104569 -0.494929 1.071804 h -2.104569 -0.706771 -1.039575
Limit the amount of filling
If we only want consecutive gaps filled up to a certain number of data points, we can use the limit keyword:
In [47]: df Out[47]: one two three a NaN -0.282863 -1.509059 c NaN 1.212112 -0.173215 e NaN NaN NaN f NaN NaN NaN h NaN -0.706771 -1.039575 In [48]: df.fillna(method='pad', limit=1) Out[48]: one two three a NaN -0.282863 -1.509059 c NaN 1.212112 -0.173215 e NaN 1.212112 -0.173215 f NaN NaN NaN h NaN -0.706771 -1.039575
To remind you, these are the available filling methods:
Method
Action
pad / ffill
Fill values forward
bfill / backfill
Fill values backward
With time series data, using pad/ffill is extremely common so that the “last known value” is available at every time point.
ffill() is equivalent to fillna(method='ffill') and bfill() is equivalent to fillna(method='bfill')
ffill()
fillna(method='ffill')
bfill()
fillna(method='bfill')
You can also fillna using a dict or Series that is alignable. The labels of the dict or index of the Series must match the columns of the frame you wish to fill. The use case of this is to fill a DataFrame with the mean of that column.
In [49]: dff = pd.DataFrame(np.random.randn(10, 3), columns=list('ABC')) In [50]: dff.iloc[3:5, 0] = np.nan In [51]: dff.iloc[4:6, 1] = np.nan In [52]: dff.iloc[5:8, 2] = np.nan In [53]: dff Out[53]: A B C 0 0.271860 -0.424972 0.567020 1 0.276232 -1.087401 -0.673690 2 0.113648 -1.478427 0.524988 3 NaN 0.577046 -1.715002 4 NaN NaN -1.157892 5 -1.344312 NaN NaN 6 -0.109050 1.643563 NaN 7 0.357021 -0.674600 NaN 8 -0.968914 -1.294524 0.413738 9 0.276662 -0.472035 -0.013960 In [54]: dff.fillna(dff.mean()) Out[54]: A B C 0 0.271860 -0.424972 0.567020 1 0.276232 -1.087401 -0.673690 2 0.113648 -1.478427 0.524988 3 -0.140857 0.577046 -1.715002 4 -0.140857 -0.401419 -1.157892 5 -1.344312 -0.401419 -0.293543 6 -0.109050 1.643563 -0.293543 7 0.357021 -0.674600 -0.293543 8 -0.968914 -1.294524 0.413738 9 0.276662 -0.472035 -0.013960 In [55]: dff.fillna(dff.mean()['B':'C']) Out[55]: A B C 0 0.271860 -0.424972 0.567020 1 0.276232 -1.087401 -0.673690 2 0.113648 -1.478427 0.524988 3 NaN 0.577046 -1.715002 4 NaN -0.401419 -1.157892 5 -1.344312 -0.401419 -0.293543 6 -0.109050 1.643563 -0.293543 7 0.357021 -0.674600 -0.293543 8 -0.968914 -1.294524 0.413738 9 0.276662 -0.472035 -0.013960
Same result as above, but is aligning the ‘fill’ value which is a Series in this case.
In [56]: dff.where(pd.notna(dff), dff.mean(), axis='columns') Out[56]: A B C 0 0.271860 -0.424972 0.567020 1 0.276232 -1.087401 -0.673690 2 0.113648 -1.478427 0.524988 3 -0.140857 0.577046 -1.715002 4 -0.140857 -0.401419 -1.157892 5 -1.344312 -0.401419 -0.293543 6 -0.109050 1.643563 -0.293543 7 0.357021 -0.674600 -0.293543 8 -0.968914 -1.294524 0.413738 9 0.276662 -0.472035 -0.013960
You may wish to simply exclude labels from a data set which refer to missing data. To do this, use dropna():
dropna()
In [57]: df Out[57]: one two three a NaN -0.282863 -1.509059 c NaN 1.212112 -0.173215 e NaN 0.000000 0.000000 f NaN 0.000000 0.000000 h NaN -0.706771 -1.039575 In [58]: df.dropna(axis=0) Out[58]: Empty DataFrame Columns: [one, two, three] Index: [] In [59]: df.dropna(axis=1) Out[59]: two three a -0.282863 -1.509059 c 1.212112 -0.173215 e 0.000000 0.000000 f 0.000000 0.000000 h -0.706771 -1.039575 In [60]: df['one'].dropna() Out[60]: Series([], Name: one, dtype: float64)
An equivalent dropna() is available for Series. DataFrame.dropna has considerably more options than Series.dropna, which can be examined in the API.
New in version 0.23.0: The limit_area keyword argument was added.
limit_area
Both Series and DataFrame objects have interpolate() that, by default, performs linear interpolation at missing data points.
interpolate()
In [61]: ts Out[61]: 2000-01-31 0.469112 2000-02-29 NaN 2000-03-31 NaN 2000-04-28 NaN 2000-05-31 NaN ... 2007-12-31 -6.950267 2008-01-31 -7.904475 2008-02-29 -6.441779 2008-03-31 -8.184940 2008-04-30 -9.011531 Freq: BM, Length: 100, dtype: float64 In [62]: ts.count() Out[62]: 66 In [63]: ts.plot() Out[63]: <matplotlib.axes._subplots.AxesSubplot at 0x7fc64878b650>
In [64]: ts.interpolate() Out[64]: 2000-01-31 0.469112 2000-02-29 0.434469 2000-03-31 0.399826 2000-04-28 0.365184 2000-05-31 0.330541 ... 2007-12-31 -6.950267 2008-01-31 -7.904475 2008-02-29 -6.441779 2008-03-31 -8.184940 2008-04-30 -9.011531 Freq: BM, Length: 100, dtype: float64 In [65]: ts.interpolate().count() Out[65]: 100 In [66]: ts.interpolate().plot() Out[66]: <matplotlib.axes._subplots.AxesSubplot at 0x7fc648722910>
Index aware interpolation is available via the method keyword:
method
In [67]: ts2 Out[67]: 2000-01-31 0.469112 2000-02-29 NaN 2002-07-31 -5.785037 2005-01-31 NaN 2008-04-30 -9.011531 dtype: float64 In [68]: ts2.interpolate() Out[68]: 2000-01-31 0.469112 2000-02-29 -2.657962 2002-07-31 -5.785037 2005-01-31 -7.398284 2008-04-30 -9.011531 dtype: float64 In [69]: ts2.interpolate(method='time') Out[69]: 2000-01-31 0.469112 2000-02-29 0.270241 2002-07-31 -5.785037 2005-01-31 -7.190866 2008-04-30 -9.011531 dtype: float64
For a floating-point index, use method='values':
method='values'
In [70]: ser Out[70]: 0.0 0.0 1.0 NaN 10.0 10.0 dtype: float64 In [71]: ser.interpolate() Out[71]: 0.0 0.0 1.0 5.0 10.0 10.0 dtype: float64 In [72]: ser.interpolate(method='values') Out[72]: 0.0 0.0 1.0 1.0 10.0 10.0 dtype: float64
You can also interpolate with a DataFrame:
In [73]: df = pd.DataFrame({'A': [1, 2.1, np.nan, 4.7, 5.6, 6.8], ....: 'B': [.25, np.nan, np.nan, 4, 12.2, 14.4]}) ....: In [74]: df Out[74]: A B 0 1.0 0.25 1 2.1 NaN 2 NaN NaN 3 4.7 4.00 4 5.6 12.20 5 6.8 14.40 In [75]: df.interpolate() Out[75]: A B 0 1.0 0.25 1 2.1 1.50 2 3.4 2.75 3 4.7 4.00 4 5.6 12.20 5 6.8 14.40
The method argument gives access to fancier interpolation methods. If you have scipy installed, you can pass the name of a 1-d interpolation routine to method. You’ll want to consult the full scipy interpolation documentation and reference guide for details. The appropriate interpolation method will depend on the type of data you are working with.
If you are dealing with a time series that is growing at an increasing rate, method='quadratic' may be appropriate.
method='quadratic'
If you have values approximating a cumulative distribution function, then method='pchip' should work well.
method='pchip'
To fill missing values with goal of smooth plotting, consider method='akima'.
method='akima'
These methods require scipy.
scipy
In [76]: df.interpolate(method='barycentric') Out[76]: A B 0 1.00 0.250 1 2.10 -7.660 2 3.53 -4.515 3 4.70 4.000 4 5.60 12.200 5 6.80 14.400 In [77]: df.interpolate(method='pchip') Out[77]: A B 0 1.00000 0.250000 1 2.10000 0.672808 2 3.43454 1.928950 3 4.70000 4.000000 4 5.60000 12.200000 5 6.80000 14.400000 In [78]: df.interpolate(method='akima') Out[78]: A B 0 1.000000 0.250000 1 2.100000 -0.873316 2 3.406667 0.320034 3 4.700000 4.000000 4 5.600000 12.200000 5 6.800000 14.400000
When interpolating via a polynomial or spline approximation, you must also specify the degree or order of the approximation:
In [79]: df.interpolate(method='spline', order=2) Out[79]: A B 0 1.000000 0.250000 1 2.100000 -0.428598 2 3.404545 1.206900 3 4.700000 4.000000 4 5.600000 12.200000 5 6.800000 14.400000 In [80]: df.interpolate(method='polynomial', order=2) Out[80]: A B 0 1.000000 0.250000 1 2.100000 -2.703846 2 3.451351 -1.453846 3 4.700000 4.000000 4 5.600000 12.200000 5 6.800000 14.400000
Compare several methods:
In [81]: np.random.seed(2) In [82]: ser = pd.Series(np.arange(1, 10.1, .25) ** 2 + np.random.randn(37)) In [83]: missing = np.array([4, 13, 14, 15, 16, 17, 18, 20, 29]) In [84]: ser[missing] = np.nan In [85]: methods = ['linear', 'quadratic', 'cubic'] In [86]: df = pd.DataFrame({m: ser.interpolate(method=m) for m in methods}) In [87]: df.plot() Out[87]: <matplotlib.axes._subplots.AxesSubplot at 0x7fc649e9f7d0>
Another use case is interpolation at new values. Suppose you have 100 observations from some distribution. And let’s suppose that you’re particularly interested in what’s happening around the middle. You can mix pandas’ reindex and interpolate methods to interpolate at the new values.
reindex
interpolate
In [88]: ser = pd.Series(np.sort(np.random.uniform(size=100))) # interpolate at new_index In [89]: new_index = ser.index | pd.Index([49.25, 49.5, 49.75, 50.25, 50.5, 50.75]) In [90]: interp_s = ser.reindex(new_index).interpolate(method='pchip') In [91]: interp_s[49:51] Out[91]: 49.00 0.471410 49.25 0.476841 49.50 0.481780 49.75 0.485998 50.00 0.489266 50.25 0.491814 50.50 0.493995 50.75 0.495763 51.00 0.497074 dtype: float64
Like other pandas fill methods, interpolate() accepts a limit keyword argument. Use this argument to limit the number of consecutive NaN values filled since the last valid observation:
limit
In [92]: ser = pd.Series([np.nan, np.nan, 5, np.nan, np.nan, ....: np.nan, 13, np.nan, np.nan]) ....: In [93]: ser Out[93]: 0 NaN 1 NaN 2 5.0 3 NaN 4 NaN 5 NaN 6 13.0 7 NaN 8 NaN dtype: float64 # fill all consecutive values in a forward direction In [94]: ser.interpolate() Out[94]: 0 NaN 1 NaN 2 5.0 3 7.0 4 9.0 5 11.0 6 13.0 7 13.0 8 13.0 dtype: float64 # fill one consecutive value in a forward direction In [95]: ser.interpolate(limit=1) Out[95]: 0 NaN 1 NaN 2 5.0 3 7.0 4 NaN 5 NaN 6 13.0 7 13.0 8 NaN dtype: float64
By default, NaN values are filled in a forward direction. Use limit_direction parameter to fill backward or from both directions.
forward
limit_direction
backward
both
# fill one consecutive value backwards In [96]: ser.interpolate(limit=1, limit_direction='backward') Out[96]: 0 NaN 1 5.0 2 5.0 3 NaN 4 NaN 5 11.0 6 13.0 7 NaN 8 NaN dtype: float64 # fill one consecutive value in both directions In [97]: ser.interpolate(limit=1, limit_direction='both') Out[97]: 0 NaN 1 5.0 2 5.0 3 7.0 4 NaN 5 11.0 6 13.0 7 13.0 8 NaN dtype: float64 # fill all consecutive values in both directions In [98]: ser.interpolate(limit_direction='both') Out[98]: 0 5.0 1 5.0 2 5.0 3 7.0 4 9.0 5 11.0 6 13.0 7 13.0 8 13.0 dtype: float64
By default, NaN values are filled whether they are inside (surrounded by) existing valid values, or outside existing valid values. Introduced in v0.23 the limit_area parameter restricts filling to either inside or outside values.
# fill one consecutive inside value in both directions In [99]: ser.interpolate(limit_direction='both', limit_area='inside', limit=1) Out[99]: 0 NaN 1 NaN 2 5.0 3 7.0 4 NaN 5 11.0 6 13.0 7 NaN 8 NaN dtype: float64 # fill all consecutive outside values backward In [100]: ser.interpolate(limit_direction='backward', limit_area='outside') Out[100]: 0 5.0 1 5.0 2 5.0 3 NaN 4 NaN 5 NaN 6 13.0 7 NaN 8 NaN dtype: float64 # fill all consecutive outside values in both directions In [101]: ser.interpolate(limit_direction='both', limit_area='outside') Out[101]: 0 5.0 1 5.0 2 5.0 3 NaN 4 NaN 5 NaN 6 13.0 7 13.0 8 13.0 dtype: float64
Often times we want to replace arbitrary values with other values.
replace() in Series and replace() in DataFrame provides an efficient yet flexible way to perform such replacements.
replace()
For a Series, you can replace a single value or a list of values by another value:
In [102]: ser = pd.Series([0., 1., 2., 3., 4.]) In [103]: ser.replace(0, 5) Out[103]: 0 5.0 1 1.0 2 2.0 3 3.0 4 4.0 dtype: float64
You can replace a list of values by a list of other values:
In [104]: ser.replace([0, 1, 2, 3, 4], [4, 3, 2, 1, 0]) Out[104]: 0 4.0 1 3.0 2 2.0 3 1.0 4 0.0 dtype: float64
You can also specify a mapping dict:
In [105]: ser.replace({0: 10, 1: 100}) Out[105]: 0 10.0 1 100.0 2 2.0 3 3.0 4 4.0 dtype: float64
For a DataFrame, you can specify individual values by column:
In [106]: df = pd.DataFrame({'a': [0, 1, 2, 3, 4], 'b': [5, 6, 7, 8, 9]}) In [107]: df.replace({'a': 0, 'b': 5}, 100) Out[107]: a b 0 100 100 1 1 6 2 2 7 3 3 8 4 4 9
Instead of replacing with specified values, you can treat all given values as missing and interpolate over them:
In [108]: ser.replace([1, 2, 3], method='pad') Out[108]: 0 0.0 1 0.0 2 0.0 3 0.0 4 4.0 dtype: float64
Python strings prefixed with the r character such as r'hello world' are so-called “raw” strings. They have different semantics regarding backslashes than strings without this prefix. Backslashes in raw strings will be interpreted as an escaped backslash, e.g., r'\' == '\\'. You should read about them if this is unclear.
r
r'hello world'
r'\' == '\\'
Replace the ‘.’ with NaN (str -> str):
In [109]: d = {'a': list(range(4)), 'b': list('ab..'), 'c': ['a', 'b', np.nan, 'd']} In [110]: df = pd.DataFrame(d) In [111]: df.replace('.', np.nan) Out[111]: a b c 0 0 a a 1 1 b b 2 2 NaN NaN 3 3 NaN d
Now do it with a regular expression that removes surrounding whitespace (regex -> regex):
In [112]: df.replace(r'\s*\.\s*', np.nan, regex=True) Out[112]: a b c 0 0 a a 1 1 b b 2 2 NaN NaN 3 3 NaN d
Replace a few different values (list -> list):
In [113]: df.replace(['a', '.'], ['b', np.nan]) Out[113]: a b c 0 0 b b 1 1 b b 2 2 NaN NaN 3 3 NaN d
list of regex -> list of regex:
In [114]: df.replace([r'\.', r'(a)'], ['dot', r'\1stuff'], regex=True) Out[114]: a b c 0 0 astuff astuff 1 1 b b 2 2 dot NaN 3 3 dot d
Only search in column 'b' (dict -> dict):
'b'
In [115]: df.replace({'b': '.'}, {'b': np.nan}) Out[115]: a b c 0 0 a a 1 1 b b 2 2 NaN NaN 3 3 NaN d
Same as the previous example, but use a regular expression for searching instead (dict of regex -> dict):
In [116]: df.replace({'b': r'\s*\.\s*'}, {'b': np.nan}, regex=True) Out[116]: a b c 0 0 a a 1 1 b b 2 2 NaN NaN 3 3 NaN d
You can pass nested dictionaries of regular expressions that use regex=True:
regex=True
In [117]: df.replace({'b': {'b': r''}}, regex=True) Out[117]: a b c 0 0 a a 1 1 b 2 2 . NaN 3 3 . d
Alternatively, you can pass the nested dictionary like so:
In [118]: df.replace(regex={'b': {r'\s*\.\s*': np.nan}}) Out[118]: a b c 0 0 a a 1 1 b b 2 2 NaN NaN 3 3 NaN d
You can also use the group of a regular expression match when replacing (dict of regex -> dict of regex), this works for lists as well.
In [119]: df.replace({'b': r'\s*(\.)\s*'}, {'b': r'\1ty'}, regex=True) Out[119]: a b c 0 0 a a 1 1 b b 2 2 .ty NaN 3 3 .ty d
You can pass a list of regular expressions, of which those that match will be replaced with a scalar (list of regex -> regex).
In [120]: df.replace([r'\s*\.\s*', r'a|b'], np.nan, regex=True) Out[120]: a b c 0 0 NaN NaN 1 1 NaN NaN 2 2 NaN NaN 3 3 NaN d
All of the regular expression examples can also be passed with the to_replace argument as the regex argument. In this case the value argument must be passed explicitly by name or regex must be a nested dictionary. The previous example, in this case, would then be:
to_replace
regex
value
In [121]: df.replace(regex=[r'\s*\.\s*', r'a|b'], value=np.nan) Out[121]: a b c 0 0 NaN NaN 1 1 NaN NaN 2 2 NaN NaN 3 3 NaN d
This can be convenient if you do not want to pass regex=True every time you want to use a regular expression.
Anywhere in the above replace examples that you see a regular expression a compiled regular expression is valid as well.
replace
replace() is similar to fillna().
In [122]: df = pd.DataFrame(np.random.randn(10, 2)) In [123]: df[np.random.rand(df.shape[0]) > 0.5] = 1.5 In [124]: df.replace(1.5, np.nan) Out[124]: 0 1 0 -0.844214 -1.021415 1 0.432396 -0.323580 2 0.423825 0.799180 3 1.262614 0.751965 4 NaN NaN 5 NaN NaN 6 -0.498174 -1.060799 7 0.591667 -0.183257 8 1.019855 -1.482465 9 NaN NaN
Replacing more than one value is possible by passing a list.
In [125]: df00 = df.iloc[0, 0] In [126]: df.replace([1.5, df00], [np.nan, 'a']) Out[126]: 0 1 0 a -1.02141 1 0.432396 -0.32358 2 0.423825 0.79918 3 1.26261 0.751965 4 NaN NaN 5 NaN NaN 6 -0.498174 -1.0608 7 0.591667 -0.183257 8 1.01985 -1.48247 9 NaN NaN In [127]: df[1].dtype Out[127]: dtype('float64')
You can also operate on the DataFrame in place:
In [128]: df.replace(1.5, np.nan, inplace=True)
When replacing multiple bool or datetime64 objects, the first argument to replace (to_replace) must match the type of the value being replaced. For example,
bool
datetime64
>>> s = pd.Series([True, False, True]) >>> s.replace({'a string': 'new value', True: False}) # raises TypeError: Cannot compare types 'ndarray(dtype=bool)' and 'str'
will raise a TypeError because one of the dict keys is not of the correct type for replacement.
TypeError
dict
However, when replacing a single object such as,
In [129]: s = pd.Series([True, False, True]) In [130]: s.replace('a string', 'another string') Out[130]: 0 True 1 False 2 True dtype: bool
the original NDFrame object will be returned untouched. We’re working on unifying this API, but for backwards compatibility reasons we cannot break the latter behavior. See GH6354 for more details.
NDFrame
While pandas supports storing arrays of integer and boolean type, these types are not capable of storing missing data. Until we can switch to using a native NA type in NumPy, we’ve established some “casting rules”. When a reindexing operation introduces missing data, the Series will be cast according to the rules introduced in the table below.
data type
Cast to
integer
float
boolean
object
no cast
For example:
In [131]: s = pd.Series(np.random.randn(5), index=[0, 2, 4, 6, 7]) In [132]: s > 0 Out[132]: 0 True 2 True 4 True 6 True 7 True dtype: bool In [133]: (s > 0).dtype Out[133]: dtype('bool') In [134]: crit = (s > 0).reindex(list(range(8))) In [135]: crit Out[135]: 0 True 1 NaN 2 True 3 NaN 4 True 5 NaN 6 True 7 True dtype: object In [136]: crit.dtype Out[136]: dtype('O')
Ordinarily NumPy will complain if you try to use an object array (even if it contains boolean values) instead of a boolean array to get or set values from an ndarray (e.g. selecting values based on some criteria). If a boolean vector contains NAs, an exception will be generated:
In [137]: reindexed = s.reindex(list(range(8))).fillna(0) In [138]: reindexed[crit] --------------------------------------------------------------------------- ValueError Traceback (most recent call last) <ipython-input-138-0dac417a4890> in <module> ----> 1 reindexed[crit] /pandas/pandas/core/series.py in __getitem__(self, key) 905 key = list(key) 906 --> 907 if com.is_bool_indexer(key): 908 key = check_bool_indexer(self.index, key) 909 /pandas/pandas/core/common.py in is_bool_indexer(key) 134 if not lib.is_bool_array(key): 135 if isna(key).any(): --> 136 raise ValueError(na_msg) 137 return False 138 return True ValueError: cannot mask with array containing NA / NaN values
However, these can be filled in using fillna() and it will work fine:
In [139]: reindexed[crit.fillna(False)] Out[139]: 0 0.126504 2 0.696198 4 0.697416 6 0.601516 7 0.003659 dtype: float64 In [140]: reindexed[crit.fillna(True)] Out[140]: 0 0.126504 1 0.000000 2 0.696198 3 0.000000 4 0.697416 5 0.000000 6 0.601516 7 0.003659 dtype: float64
Pandas provides a nullable integer dtype, but you must explicitly request it when creating the series or column. Notice that we use a capital “I” in the dtype="Int64".
dtype="Int64"
In [141]: s = pd.Series([0, 1, np.nan, 3, 4], dtype="Int64") In [142]: s Out[142]: 0 0 1 1 2 <NA> 3 3 4 4 dtype: Int64
Experimental: the behaviour of pd.NA can still change without warning.
pd.NA
New in version 1.0.0.
Starting from pandas 1.0, an experimental pd.NA value (singleton) is available to represent scalar missing values. At this moment, it is used in the nullable integer, boolean and dedicated string data types as the missing value indicator.
The goal of pd.NA is provide a “missing” indicator that can be used consistently across data types (instead of np.nan, None or pd.NaT depending on the data type).
pd.NaT
For example, when having missing values in a Series with the nullable integer dtype, it will use pd.NA:
In [143]: s = pd.Series([1, 2, None], dtype="Int64") In [144]: s Out[144]: 0 1 1 2 2 <NA> dtype: Int64 In [145]: s[2] Out[145]: <NA> In [146]: s[2] is pd.NA Out[146]: True
Currently, pandas does not yet use those data types by default (when creating a DataFrame or Series, or when reading in data), so you need to specify the dtype explicitly. An easy way to convert to those dtypes is explained here.
In general, missing values propagate in operations involving pd.NA. When one of the operands is unknown, the outcome of the operation is also unknown.
For example, pd.NA propagates in arithmetic operations, similarly to np.nan:
In [147]: pd.NA + 1 Out[147]: <NA> In [148]: "a" * pd.NA Out[148]: <NA>
There are a few special cases when the result is known, even when one of the operands is NA.
In [149]: pd.NA ** 0 Out[149]: 1 In [150]: 1 ** pd.NA Out[150]: 1
In equality and comparison operations, pd.NA also propagates. This deviates from the behaviour of np.nan, where comparisons with np.nan always return False.
False
In [151]: pd.NA == 1 Out[151]: <NA> In [152]: pd.NA == pd.NA Out[152]: <NA> In [153]: pd.NA < 2.5 Out[153]: <NA>
To check if a value is equal to pd.NA, the isna() function can be used:
In [154]: pd.isna(pd.NA) Out[154]: True
An exception on this basic propagation rule are reductions (such as the mean or the minimum), where pandas defaults to skipping missing values. See above for more.
For logical operations, pd.NA follows the rules of the three-valued logic (or Kleene logic, similarly to R, SQL and Julia). This logic means to only propagate missing values when it is logically required.
For example, for the logical “or” operation (|), if one of the operands is True, we already know the result will be True, regardless of the other value (so regardless the missing value would be True or False). In this case, pd.NA does not propagate:
|
True
In [155]: True | False Out[155]: True In [156]: True | pd.NA Out[156]: True In [157]: pd.NA | True Out[157]: True
On the other hand, if one of the operands is False, the result depends on the value of the other operand. Therefore, in this case pd.NA propagates:
In [158]: False | True Out[158]: True In [159]: False | False Out[159]: False In [160]: False | pd.NA Out[160]: <NA>
The behaviour of the logical “and” operation (&) can be derived using similar logic (where now pd.NA will not propagate if one of the operands is already False):
&
In [161]: False & True Out[161]: False In [162]: False & False Out[162]: False In [163]: False & pd.NA Out[163]: False
In [164]: True & True Out[164]: True In [165]: True & False Out[165]: False In [166]: True & pd.NA Out[166]: <NA>
Since the actual value of an NA is unknown, it is ambiguous to convert NA to a boolean value. The following raises an error:
In [167]: bool(pd.NA) --------------------------------------------------------------------------- TypeError Traceback (most recent call last) <ipython-input-167-5477a57d5abb> in <module> ----> 1 bool(pd.NA) /pandas/pandas/_libs/missing.pyx in pandas._libs.missing.NAType.__bool__() TypeError: boolean value of NA is ambiguous
This also means that pd.NA cannot be used in a context where it is evaluated to a boolean, such as if condition: ... where condition can potentially be pd.NA. In such cases, isna() can be used to check for pd.NA or condition being pd.NA can be avoided, for example by filling missing values beforehand.
if condition: ...
condition
A similar situation occurs when using Series or DataFrame objects in if statements, see Using if/truth statements with pandas.
if
pandas.NA implements NumPy’s __array_ufunc__ protocol. Most ufuncs work with NA, and generally return NA:
pandas.NA
__array_ufunc__
In [168]: np.log(pd.NA) Out[168]: <NA> In [169]: np.add(pd.NA, 1) Out[169]: <NA>
Currently, ufuncs involving an ndarray and NA will return an object-dtype filled with NA values.
In [170]: a = np.array([1, 2, 3]) In [171]: np.greater(a, pd.NA) Out[171]: array([<NA>, <NA>, <NA>], dtype=object)
The return type here may change to return a different array type in the future.
See DataFrame interoperability with NumPy functions for more on ufuncs.
If you have a DataFrame or Series using traditional types that have missing data represented using np.nan, there are convenience methods convert_dtypes() in Series and convert_dtypes() in DataFrame that can convert data to use the newer dtypes for integers, strings and booleans listed here. This is especially helpful after reading in data sets when letting the readers such as read_csv() and read_excel() infer default dtypes.
convert_dtypes()
read_csv()
read_excel()
In this example, while the dtypes of all columns are changed, we show the results for the first 10 columns.
In [172]: bb = pd.read_csv('data/baseball.csv', index_col='id') In [173]: bb[bb.columns[:10]].dtypes Out[173]: player object year int64 stint int64 team object lg object g int64 ab int64 r int64 h int64 X2b int64 dtype: object
In [174]: bbn = bb.convert_dtypes() In [175]: bbn[bbn.columns[:10]].dtypes Out[175]: player string year Int64 stint Int64 team string lg string g Int64 ab Int64 r Int64 h Int64 X2b Int64 dtype: object