This is a short introduction to pandas, geared mainly for new users. You can see more complex recipes in the Cookbook.
Customarily, we import as follows:
In [1]: import numpy as np In [2]: import pandas as pd
See the Data Structure Intro section.
Creating a Series by passing a list of values, letting pandas create a default integer index:
Series
In [3]: s = pd.Series([1, 3, 5, np.nan, 6, 8]) In [4]: s Out[4]: 0 1.0 1 3.0 2 5.0 3 NaN 4 6.0 5 8.0 dtype: float64
Creating a DataFrame by passing a NumPy array, with a datetime index and labeled columns:
DataFrame
In [5]: dates = pd.date_range("20130101", periods=6) In [6]: dates Out[6]: DatetimeIndex(['2013-01-01', '2013-01-02', '2013-01-03', '2013-01-04', '2013-01-05', '2013-01-06'], dtype='datetime64[ns]', freq='D') In [7]: df = pd.DataFrame(np.random.randn(6, 4), index=dates, columns=list("ABCD")) In [8]: df Out[8]: A B C D 2013-01-01 0.469112 -0.282863 -1.509059 -1.135632 2013-01-02 1.212112 -0.173215 0.119209 -1.044236 2013-01-03 -0.861849 -2.104569 -0.494929 1.071804 2013-01-04 0.721555 -0.706771 -1.039575 0.271860 2013-01-05 -0.424972 0.567020 0.276232 -1.087401 2013-01-06 -0.673690 0.113648 -1.478427 0.524988
Creating a DataFrame by passing a dict of objects that can be converted to series-like.
In [9]: df2 = pd.DataFrame( ...: { ...: "A": 1.0, ...: "B": pd.Timestamp("20130102"), ...: "C": pd.Series(1, index=list(range(4)), dtype="float32"), ...: "D": np.array([3] * 4, dtype="int32"), ...: "E": pd.Categorical(["test", "train", "test", "train"]), ...: "F": "foo", ...: } ...: ) ...: In [10]: df2 Out[10]: A B C D E F 0 1.0 2013-01-02 1.0 3 test foo 1 1.0 2013-01-02 1.0 3 train foo 2 1.0 2013-01-02 1.0 3 test foo 3 1.0 2013-01-02 1.0 3 train foo
The columns of the resulting DataFrame have different dtypes.
In [11]: df2.dtypes Out[11]: A float64 B datetime64[ns] C float32 D int32 E category F object dtype: object
If you’re using IPython, tab completion for column names (as well as public attributes) is automatically enabled. Here’s a subset of the attributes that will be completed:
In [12]: df2.<TAB> # noqa: E225, E999 df2.A df2.bool df2.abs df2.boxplot df2.add df2.C df2.add_prefix df2.clip df2.add_suffix df2.columns df2.align df2.copy df2.all df2.count df2.any df2.combine df2.append df2.D df2.apply df2.describe df2.applymap df2.diff df2.B df2.duplicated
As you can see, the columns A, B, C, and D are automatically tab completed. E and F are there as well; the rest of the attributes have been truncated for brevity.
A
B
C
D
E
F
See the Basics section.
Here is how to view the top and bottom rows of the frame:
In [13]: df.head() Out[13]: A B C D 2013-01-01 0.469112 -0.282863 -1.509059 -1.135632 2013-01-02 1.212112 -0.173215 0.119209 -1.044236 2013-01-03 -0.861849 -2.104569 -0.494929 1.071804 2013-01-04 0.721555 -0.706771 -1.039575 0.271860 2013-01-05 -0.424972 0.567020 0.276232 -1.087401 In [14]: df.tail(3) Out[14]: A B C D 2013-01-04 0.721555 -0.706771 -1.039575 0.271860 2013-01-05 -0.424972 0.567020 0.276232 -1.087401 2013-01-06 -0.673690 0.113648 -1.478427 0.524988
Display the index, columns:
In [15]: df.index Out[15]: DatetimeIndex(['2013-01-01', '2013-01-02', '2013-01-03', '2013-01-04', '2013-01-05', '2013-01-06'], dtype='datetime64[ns]', freq='D') In [16]: df.columns Out[16]: Index(['A', 'B', 'C', 'D'], dtype='object')
DataFrame.to_numpy() gives a NumPy representation of the underlying data. Note that this can be an expensive operation when your DataFrame has columns with different data types, which comes down to a fundamental difference between pandas and NumPy: NumPy arrays have one dtype for the entire array, while pandas DataFrames have one dtype per column. When you call DataFrame.to_numpy(), pandas will find the NumPy dtype that can hold all of the dtypes in the DataFrame. This may end up being object, which requires casting every value to a Python object.
DataFrame.to_numpy()
object
For df, our DataFrame of all floating-point values, DataFrame.to_numpy() is fast and doesn’t require copying data.
df
In [17]: df.to_numpy() Out[17]: array([[ 0.4691, -0.2829, -1.5091, -1.1356], [ 1.2121, -0.1732, 0.1192, -1.0442], [-0.8618, -2.1046, -0.4949, 1.0718], [ 0.7216, -0.7068, -1.0396, 0.2719], [-0.425 , 0.567 , 0.2762, -1.0874], [-0.6737, 0.1136, -1.4784, 0.525 ]])
For df2, the DataFrame with multiple dtypes, DataFrame.to_numpy() is relatively expensive.
df2
In [18]: df2.to_numpy() Out[18]: array([[1.0, Timestamp('2013-01-02 00:00:00'), 1.0, 3, 'test', 'foo'], [1.0, Timestamp('2013-01-02 00:00:00'), 1.0, 3, 'train', 'foo'], [1.0, Timestamp('2013-01-02 00:00:00'), 1.0, 3, 'test', 'foo'], [1.0, Timestamp('2013-01-02 00:00:00'), 1.0, 3, 'train', 'foo']], dtype=object)
Note
DataFrame.to_numpy() does not include the index or column labels in the output.
describe() shows a quick statistic summary of your data:
describe()
In [19]: df.describe() Out[19]: A B C D count 6.000000 6.000000 6.000000 6.000000 mean 0.073711 -0.431125 -0.687758 -0.233103 std 0.843157 0.922818 0.779887 0.973118 min -0.861849 -2.104569 -1.509059 -1.135632 25% -0.611510 -0.600794 -1.368714 -1.076610 50% 0.022070 -0.228039 -0.767252 -0.386188 75% 0.658444 0.041933 -0.034326 0.461706 max 1.212112 0.567020 0.276232 1.071804
Transposing your data:
In [20]: df.T Out[20]: 2013-01-01 2013-01-02 2013-01-03 2013-01-04 2013-01-05 2013-01-06 A 0.469112 1.212112 -0.861849 0.721555 -0.424972 -0.673690 B -0.282863 -0.173215 -2.104569 -0.706771 0.567020 0.113648 C -1.509059 0.119209 -0.494929 -1.039575 0.276232 -1.478427 D -1.135632 -1.044236 1.071804 0.271860 -1.087401 0.524988
Sorting by an axis:
In [21]: df.sort_index(axis=1, ascending=False) Out[21]: D C B A 2013-01-01 -1.135632 -1.509059 -0.282863 0.469112 2013-01-02 -1.044236 0.119209 -0.173215 1.212112 2013-01-03 1.071804 -0.494929 -2.104569 -0.861849 2013-01-04 0.271860 -1.039575 -0.706771 0.721555 2013-01-05 -1.087401 0.276232 0.567020 -0.424972 2013-01-06 0.524988 -1.478427 0.113648 -0.673690
Sorting by values:
In [22]: df.sort_values(by="B") Out[22]: A B C D 2013-01-03 -0.861849 -2.104569 -0.494929 1.071804 2013-01-04 0.721555 -0.706771 -1.039575 0.271860 2013-01-01 0.469112 -0.282863 -1.509059 -1.135632 2013-01-02 1.212112 -0.173215 0.119209 -1.044236 2013-01-06 -0.673690 0.113648 -1.478427 0.524988 2013-01-05 -0.424972 0.567020 0.276232 -1.087401
While standard Python / Numpy expressions for selecting and setting are intuitive and come in handy for interactive work, for production code, we recommend the optimized pandas data access methods, .at, .iat, .loc and .iloc.
.at
.iat
.loc
.iloc
See the indexing documentation Indexing and Selecting Data and MultiIndex / Advanced Indexing.
Selecting a single column, which yields a Series, equivalent to df.A:
df.A
In [23]: df["A"] Out[23]: 2013-01-01 0.469112 2013-01-02 1.212112 2013-01-03 -0.861849 2013-01-04 0.721555 2013-01-05 -0.424972 2013-01-06 -0.673690 Freq: D, Name: A, dtype: float64
Selecting via [], which slices the rows.
[]
In [24]: df[0:3] Out[24]: A B C D 2013-01-01 0.469112 -0.282863 -1.509059 -1.135632 2013-01-02 1.212112 -0.173215 0.119209 -1.044236 2013-01-03 -0.861849 -2.104569 -0.494929 1.071804 In [25]: df["20130102":"20130104"] Out[25]: A B C D 2013-01-02 1.212112 -0.173215 0.119209 -1.044236 2013-01-03 -0.861849 -2.104569 -0.494929 1.071804 2013-01-04 0.721555 -0.706771 -1.039575 0.271860
See more in Selection by Label.
For getting a cross section using a label:
In [26]: df.loc[dates[0]] Out[26]: A 0.469112 B -0.282863 C -1.509059 D -1.135632 Name: 2013-01-01 00:00:00, dtype: float64
Selecting on a multi-axis by label:
In [27]: df.loc[:, ["A", "B"]] Out[27]: A B 2013-01-01 0.469112 -0.282863 2013-01-02 1.212112 -0.173215 2013-01-03 -0.861849 -2.104569 2013-01-04 0.721555 -0.706771 2013-01-05 -0.424972 0.567020 2013-01-06 -0.673690 0.113648
Showing label slicing, both endpoints are included:
In [28]: df.loc["20130102":"20130104", ["A", "B"]] Out[28]: A B 2013-01-02 1.212112 -0.173215 2013-01-03 -0.861849 -2.104569 2013-01-04 0.721555 -0.706771
Reduction in the dimensions of the returned object:
In [29]: df.loc["20130102", ["A", "B"]] Out[29]: A 1.212112 B -0.173215 Name: 2013-01-02 00:00:00, dtype: float64
For getting a scalar value:
In [30]: df.loc[dates[0], "A"] Out[30]: 0.4691122999071863
For getting fast access to a scalar (equivalent to the prior method):
In [31]: df.at[dates[0], "A"] Out[31]: 0.4691122999071863
See more in Selection by Position.
Select via the position of the passed integers:
In [32]: df.iloc[3] Out[32]: A 0.721555 B -0.706771 C -1.039575 D 0.271860 Name: 2013-01-04 00:00:00, dtype: float64
By integer slices, acting similar to numpy/Python:
In [33]: df.iloc[3:5, 0:2] Out[33]: A B 2013-01-04 0.721555 -0.706771 2013-01-05 -0.424972 0.567020
By lists of integer position locations, similar to the NumPy/Python style:
In [34]: df.iloc[[1, 2, 4], [0, 2]] Out[34]: A C 2013-01-02 1.212112 0.119209 2013-01-03 -0.861849 -0.494929 2013-01-05 -0.424972 0.276232
For slicing rows explicitly:
In [35]: df.iloc[1:3, :] Out[35]: A B C D 2013-01-02 1.212112 -0.173215 0.119209 -1.044236 2013-01-03 -0.861849 -2.104569 -0.494929 1.071804
For slicing columns explicitly:
In [36]: df.iloc[:, 1:3] Out[36]: B C 2013-01-01 -0.282863 -1.509059 2013-01-02 -0.173215 0.119209 2013-01-03 -2.104569 -0.494929 2013-01-04 -0.706771 -1.039575 2013-01-05 0.567020 0.276232 2013-01-06 0.113648 -1.478427
For getting a value explicitly:
In [37]: df.iloc[1, 1] Out[37]: -0.17321464905330858
In [38]: df.iat[1, 1] Out[38]: -0.17321464905330858
Using a single column’s values to select data.
In [39]: df[df["A"] > 0] Out[39]: A B C D 2013-01-01 0.469112 -0.282863 -1.509059 -1.135632 2013-01-02 1.212112 -0.173215 0.119209 -1.044236 2013-01-04 0.721555 -0.706771 -1.039575 0.271860
Selecting values from a DataFrame where a boolean condition is met.
In [40]: df[df > 0] Out[40]: A B C D 2013-01-01 0.469112 NaN NaN NaN 2013-01-02 1.212112 NaN 0.119209 NaN 2013-01-03 NaN NaN NaN 1.071804 2013-01-04 0.721555 NaN NaN 0.271860 2013-01-05 NaN 0.567020 0.276232 NaN 2013-01-06 NaN 0.113648 NaN 0.524988
Using the isin() method for filtering:
isin()
In [41]: df2 = df.copy() In [42]: df2["E"] = ["one", "one", "two", "three", "four", "three"] In [43]: df2 Out[43]: A B C D E 2013-01-01 0.469112 -0.282863 -1.509059 -1.135632 one 2013-01-02 1.212112 -0.173215 0.119209 -1.044236 one 2013-01-03 -0.861849 -2.104569 -0.494929 1.071804 two 2013-01-04 0.721555 -0.706771 -1.039575 0.271860 three 2013-01-05 -0.424972 0.567020 0.276232 -1.087401 four 2013-01-06 -0.673690 0.113648 -1.478427 0.524988 three In [44]: df2[df2["E"].isin(["two", "four"])] Out[44]: A B C D E 2013-01-03 -0.861849 -2.104569 -0.494929 1.071804 two 2013-01-05 -0.424972 0.567020 0.276232 -1.087401 four
Setting a new column automatically aligns the data by the indexes.
In [45]: s1 = pd.Series([1, 2, 3, 4, 5, 6], index=pd.date_range("20130102", periods=6)) In [46]: s1 Out[46]: 2013-01-02 1 2013-01-03 2 2013-01-04 3 2013-01-05 4 2013-01-06 5 2013-01-07 6 Freq: D, dtype: int64 In [47]: df["F"] = s1
Setting values by label:
In [48]: df.at[dates[0], "A"] = 0
Setting values by position:
In [49]: df.iat[0, 1] = 0
Setting by assigning with a NumPy array:
In [50]: df.loc[:, "D"] = np.array([5] * len(df))
The result of the prior setting operations.
In [51]: df Out[51]: A B C D F 2013-01-01 0.000000 0.000000 -1.509059 5 NaN 2013-01-02 1.212112 -0.173215 0.119209 5 1.0 2013-01-03 -0.861849 -2.104569 -0.494929 5 2.0 2013-01-04 0.721555 -0.706771 -1.039575 5 3.0 2013-01-05 -0.424972 0.567020 0.276232 5 4.0 2013-01-06 -0.673690 0.113648 -1.478427 5 5.0
A where operation with setting.
where
In [52]: df2 = df.copy() In [53]: df2[df2 > 0] = -df2 In [54]: df2 Out[54]: A B C D F 2013-01-01 0.000000 0.000000 -1.509059 -5 NaN 2013-01-02 -1.212112 -0.173215 -0.119209 -5 -1.0 2013-01-03 -0.861849 -2.104569 -0.494929 -5 -2.0 2013-01-04 -0.721555 -0.706771 -1.039575 -5 -3.0 2013-01-05 -0.424972 -0.567020 -0.276232 -5 -4.0 2013-01-06 -0.673690 -0.113648 -1.478427 -5 -5.0
pandas primarily uses the value np.nan to represent missing data. It is by default not included in computations. See the Missing Data section.
np.nan
Reindexing allows you to change/add/delete the index on a specified axis. This returns a copy of the data.
In [55]: df1 = df.reindex(index=dates[0:4], columns=list(df.columns) + ["E"]) In [56]: df1.loc[dates[0] : dates[1], "E"] = 1 In [57]: df1 Out[57]: A B C D F E 2013-01-01 0.000000 0.000000 -1.509059 5 NaN 1.0 2013-01-02 1.212112 -0.173215 0.119209 5 1.0 1.0 2013-01-03 -0.861849 -2.104569 -0.494929 5 2.0 NaN 2013-01-04 0.721555 -0.706771 -1.039575 5 3.0 NaN
To drop any rows that have missing data.
In [58]: df1.dropna(how="any") Out[58]: A B C D F E 2013-01-02 1.212112 -0.173215 0.119209 5 1.0 1.0
Filling missing data.
In [59]: df1.fillna(value=5) Out[59]: A B C D F E 2013-01-01 0.000000 0.000000 -1.509059 5 5.0 1.0 2013-01-02 1.212112 -0.173215 0.119209 5 1.0 1.0 2013-01-03 -0.861849 -2.104569 -0.494929 5 2.0 5.0 2013-01-04 0.721555 -0.706771 -1.039575 5 3.0 5.0
To get the boolean mask where values are nan.
nan
In [60]: pd.isna(df1) Out[60]: A B C D F E 2013-01-01 False False False False True False 2013-01-02 False False False False False False 2013-01-03 False False False False False True 2013-01-04 False False False False False True
See the Basic section on Binary Ops.
Operations in general exclude missing data.
Performing a descriptive statistic:
In [61]: df.mean() Out[61]: A -0.004474 B -0.383981 C -0.687758 D 5.000000 F 3.000000 dtype: float64
Same operation on the other axis:
In [62]: df.mean(1) Out[62]: 2013-01-01 0.872735 2013-01-02 1.431621 2013-01-03 0.707731 2013-01-04 1.395042 2013-01-05 1.883656 2013-01-06 1.592306 Freq: D, dtype: float64
Operating with objects that have different dimensionality and need alignment. In addition, pandas automatically broadcasts along the specified dimension.
In [63]: s = pd.Series([1, 3, 5, np.nan, 6, 8], index=dates).shift(2) In [64]: s Out[64]: 2013-01-01 NaN 2013-01-02 NaN 2013-01-03 1.0 2013-01-04 3.0 2013-01-05 5.0 2013-01-06 NaN Freq: D, dtype: float64 In [65]: df.sub(s, axis="index") Out[65]: A B C D F 2013-01-01 NaN NaN NaN NaN NaN 2013-01-02 NaN NaN NaN NaN NaN 2013-01-03 -1.861849 -3.104569 -1.494929 4.0 1.0 2013-01-04 -2.278445 -3.706771 -4.039575 2.0 0.0 2013-01-05 -5.424972 -4.432980 -4.723768 0.0 -1.0 2013-01-06 NaN NaN NaN NaN NaN
Applying functions to the data:
In [66]: df.apply(np.cumsum) Out[66]: A B C D F 2013-01-01 0.000000 0.000000 -1.509059 5 NaN 2013-01-02 1.212112 -0.173215 -1.389850 10 1.0 2013-01-03 0.350263 -2.277784 -1.884779 15 3.0 2013-01-04 1.071818 -2.984555 -2.924354 20 6.0 2013-01-05 0.646846 -2.417535 -2.648122 25 10.0 2013-01-06 -0.026844 -2.303886 -4.126549 30 15.0 In [67]: df.apply(lambda x: x.max() - x.min()) Out[67]: A 2.073961 B 2.671590 C 1.785291 D 0.000000 F 4.000000 dtype: float64
See more at Histogramming and Discretization.
In [68]: s = pd.Series(np.random.randint(0, 7, size=10)) In [69]: s Out[69]: 0 4 1 2 2 1 3 2 4 6 5 4 6 4 7 6 8 4 9 4 dtype: int64 In [70]: s.value_counts() Out[70]: 4 5 2 2 6 2 1 1 dtype: int64
Series is equipped with a set of string processing methods in the str attribute that make it easy to operate on each element of the array, as in the code snippet below. Note that pattern-matching in str generally uses regular expressions by default (and in some cases always uses them). See more at Vectorized String Methods.
str
In [71]: s = pd.Series(["A", "B", "C", "Aaba", "Baca", np.nan, "CABA", "dog", "cat"]) In [72]: s.str.lower() Out[72]: 0 a 1 b 2 c 3 aaba 4 baca 5 NaN 6 caba 7 dog 8 cat dtype: object
pandas provides various facilities for easily combining together Series and DataFrame objects with various kinds of set logic for the indexes and relational algebra functionality in the case of join / merge-type operations.
See the Merging section.
Concatenating pandas objects together with concat():
concat()
In [73]: df = pd.DataFrame(np.random.randn(10, 4)) In [74]: df Out[74]: 0 1 2 3 0 -0.548702 1.467327 -1.015962 -0.483075 1 1.637550 -1.217659 -0.291519 -1.745505 2 -0.263952 0.991460 -0.919069 0.266046 3 -0.709661 1.669052 1.037882 -1.705775 4 -0.919854 -0.042379 1.247642 -0.009920 5 0.290213 0.495767 0.362949 1.548106 6 -1.131345 -0.089329 0.337863 -0.945867 7 -0.932132 1.956030 0.017587 -0.016692 8 -0.575247 0.254161 -1.143704 0.215897 9 1.193555 -0.077118 -0.408530 -0.862495 # break it into pieces In [75]: pieces = [df[:3], df[3:7], df[7:]] In [76]: pd.concat(pieces) Out[76]: 0 1 2 3 0 -0.548702 1.467327 -1.015962 -0.483075 1 1.637550 -1.217659 -0.291519 -1.745505 2 -0.263952 0.991460 -0.919069 0.266046 3 -0.709661 1.669052 1.037882 -1.705775 4 -0.919854 -0.042379 1.247642 -0.009920 5 0.290213 0.495767 0.362949 1.548106 6 -1.131345 -0.089329 0.337863 -0.945867 7 -0.932132 1.956030 0.017587 -0.016692 8 -0.575247 0.254161 -1.143704 0.215897 9 1.193555 -0.077118 -0.408530 -0.862495
Adding a column to a DataFrame is relatively fast. However, adding a row requires a copy, and may be expensive. We recommend passing a pre-built list of records to the DataFrame constructor instead of building a DataFrame by iteratively appending records to it. See Appending to dataframe for more.
SQL style merges. See the Database style joining section.
In [77]: left = pd.DataFrame({"key": ["foo", "foo"], "lval": [1, 2]}) In [78]: right = pd.DataFrame({"key": ["foo", "foo"], "rval": [4, 5]}) In [79]: left Out[79]: key lval 0 foo 1 1 foo 2 In [80]: right Out[80]: key rval 0 foo 4 1 foo 5 In [81]: pd.merge(left, right, on="key") Out[81]: key lval rval 0 foo 1 4 1 foo 1 5 2 foo 2 4 3 foo 2 5
Another example that can be given is:
In [82]: left = pd.DataFrame({"key": ["foo", "bar"], "lval": [1, 2]}) In [83]: right = pd.DataFrame({"key": ["foo", "bar"], "rval": [4, 5]}) In [84]: left Out[84]: key lval 0 foo 1 1 bar 2 In [85]: right Out[85]: key rval 0 foo 4 1 bar 5 In [86]: pd.merge(left, right, on="key") Out[86]: key lval rval 0 foo 1 4 1 bar 2 5
By “group by” we are referring to a process involving one or more of the following steps:
Splitting the data into groups based on some criteria Applying a function to each group independently Combining the results into a data structure
Splitting the data into groups based on some criteria
Applying a function to each group independently
Combining the results into a data structure
See the Grouping section.
In [87]: df = pd.DataFrame( ....: { ....: "A": ["foo", "bar", "foo", "bar", "foo", "bar", "foo", "foo"], ....: "B": ["one", "one", "two", "three", "two", "two", "one", "three"], ....: "C": np.random.randn(8), ....: "D": np.random.randn(8), ....: } ....: ) ....: In [88]: df Out[88]: A B C D 0 foo one 1.346061 -1.577585 1 bar one 1.511763 0.396823 2 foo two 1.627081 -0.105381 3 bar three -0.990582 -0.532532 4 foo two -0.441652 1.453749 5 bar two 1.211526 1.208843 6 foo one 0.268520 -0.080952 7 foo three 0.024580 -0.264610
Grouping and then applying the sum() function to the resulting groups.
sum()
In [89]: df.groupby("A").sum() Out[89]: C D A bar 1.732707 1.073134 foo 2.824590 -0.574779
Grouping by multiple columns forms a hierarchical index, and again we can apply the sum() function.
In [90]: df.groupby(["A", "B"]).sum() Out[90]: C D A B bar one 1.511763 0.396823 three -0.990582 -0.532532 two 1.211526 1.208843 foo one 1.614581 -1.658537 three 0.024580 -0.264610 two 1.185429 1.348368
See the sections on Hierarchical Indexing and Reshaping.
In [91]: tuples = list( ....: zip( ....: *[ ....: ["bar", "bar", "baz", "baz", "foo", "foo", "qux", "qux"], ....: ["one", "two", "one", "two", "one", "two", "one", "two"], ....: ] ....: ) ....: ) ....: In [92]: index = pd.MultiIndex.from_tuples(tuples, names=["first", "second"]) In [93]: df = pd.DataFrame(np.random.randn(8, 2), index=index, columns=["A", "B"]) In [94]: df2 = df[:4] In [95]: df2 Out[95]: A B first second bar one -0.727965 -0.589346 two 0.339969 -0.693205 baz one -0.339355 0.593616 two 0.884345 1.591431
The stack() method “compresses” a level in the DataFrame’s columns.
stack()
In [96]: stacked = df2.stack() In [97]: stacked Out[97]: first second bar one A -0.727965 B -0.589346 two A 0.339969 B -0.693205 baz one A -0.339355 B 0.593616 two A 0.884345 B 1.591431 dtype: float64
With a “stacked” DataFrame or Series (having a MultiIndex as the index), the inverse operation of stack() is unstack(), which by default unstacks the last level:
MultiIndex
index
unstack()
In [98]: stacked.unstack() Out[98]: A B first second bar one -0.727965 -0.589346 two 0.339969 -0.693205 baz one -0.339355 0.593616 two 0.884345 1.591431 In [99]: stacked.unstack(1) Out[99]: second one two first bar A -0.727965 0.339969 B -0.589346 -0.693205 baz A -0.339355 0.884345 B 0.593616 1.591431 In [100]: stacked.unstack(0) Out[100]: first bar baz second one A -0.727965 -0.339355 B -0.589346 0.593616 two A 0.339969 0.884345 B -0.693205 1.591431
See the section on Pivot Tables.
In [101]: df = pd.DataFrame( .....: { .....: "A": ["one", "one", "two", "three"] * 3, .....: "B": ["A", "B", "C"] * 4, .....: "C": ["foo", "foo", "foo", "bar", "bar", "bar"] * 2, .....: "D": np.random.randn(12), .....: "E": np.random.randn(12), .....: } .....: ) .....: In [102]: df Out[102]: A B C D E 0 one A foo -1.202872 0.047609 1 one B foo -1.814470 -0.136473 2 two C foo 1.018601 -0.561757 3 three A bar -0.595447 -1.623033 4 one B bar 1.395433 0.029399 5 one C bar -0.392670 -0.542108 6 two A foo 0.007207 0.282696 7 three B foo 1.928123 -0.087302 8 one C foo -0.055224 -1.575170 9 one A bar 2.395985 1.771208 10 two B bar 1.552825 0.816482 11 three C bar 0.166599 1.100230
We can produce pivot tables from this data very easily:
In [103]: pd.pivot_table(df, values="D", index=["A", "B"], columns=["C"]) Out[103]: C bar foo A B one A 2.395985 -1.202872 B 1.395433 -1.814470 C -0.392670 -0.055224 three A -0.595447 NaN B NaN 1.928123 C 0.166599 NaN two A NaN 0.007207 B 1.552825 NaN C NaN 1.018601
pandas has simple, powerful, and efficient functionality for performing resampling operations during frequency conversion (e.g., converting secondly data into 5-minutely data). This is extremely common in, but not limited to, financial applications. See the Time Series section.
In [104]: rng = pd.date_range("1/1/2012", periods=100, freq="S") In [105]: ts = pd.Series(np.random.randint(0, 500, len(rng)), index=rng) In [106]: ts.resample("5Min").sum() Out[106]: 2012-01-01 24182 Freq: 5T, dtype: int64
Time zone representation:
In [107]: rng = pd.date_range("3/6/2012 00:00", periods=5, freq="D") In [108]: ts = pd.Series(np.random.randn(len(rng)), rng) In [109]: ts Out[109]: 2012-03-06 1.857704 2012-03-07 -1.193545 2012-03-08 0.677510 2012-03-09 -0.153931 2012-03-10 0.520091 Freq: D, dtype: float64 In [110]: ts_utc = ts.tz_localize("UTC") In [111]: ts_utc Out[111]: 2012-03-06 00:00:00+00:00 1.857704 2012-03-07 00:00:00+00:00 -1.193545 2012-03-08 00:00:00+00:00 0.677510 2012-03-09 00:00:00+00:00 -0.153931 2012-03-10 00:00:00+00:00 0.520091 Freq: D, dtype: float64
Converting to another time zone:
In [112]: ts_utc.tz_convert("US/Eastern") Out[112]: 2012-03-05 19:00:00-05:00 1.857704 2012-03-06 19:00:00-05:00 -1.193545 2012-03-07 19:00:00-05:00 0.677510 2012-03-08 19:00:00-05:00 -0.153931 2012-03-09 19:00:00-05:00 0.520091 Freq: D, dtype: float64
Converting between time span representations:
In [113]: rng = pd.date_range("1/1/2012", periods=5, freq="M") In [114]: ts = pd.Series(np.random.randn(len(rng)), index=rng) In [115]: ts Out[115]: 2012-01-31 -1.475051 2012-02-29 0.722570 2012-03-31 -0.322646 2012-04-30 -1.601631 2012-05-31 0.778033 Freq: M, dtype: float64 In [116]: ps = ts.to_period() In [117]: ps Out[117]: 2012-01 -1.475051 2012-02 0.722570 2012-03 -0.322646 2012-04 -1.601631 2012-05 0.778033 Freq: M, dtype: float64 In [118]: ps.to_timestamp() Out[118]: 2012-01-01 -1.475051 2012-02-01 0.722570 2012-03-01 -0.322646 2012-04-01 -1.601631 2012-05-01 0.778033 Freq: MS, dtype: float64
Converting between period and timestamp enables some convenient arithmetic functions to be used. In the following example, we convert a quarterly frequency with year ending in November to 9am of the end of the month following the quarter end:
In [119]: prng = pd.period_range("1990Q1", "2000Q4", freq="Q-NOV") In [120]: ts = pd.Series(np.random.randn(len(prng)), prng) In [121]: ts.index = (prng.asfreq("M", "e") + 1).asfreq("H", "s") + 9 In [122]: ts.head() Out[122]: 1990-03-01 09:00 -0.289342 1990-06-01 09:00 0.233141 1990-09-01 09:00 -0.223540 1990-12-01 09:00 0.542054 1991-03-01 09:00 -0.688585 Freq: H, dtype: float64
pandas can include categorical data in a DataFrame. For full docs, see the categorical introduction and the API documentation.
In [123]: df = pd.DataFrame( .....: {"id": [1, 2, 3, 4, 5, 6], "raw_grade": ["a", "b", "b", "a", "a", "e"]} .....: ) .....:
Convert the raw grades to a categorical data type.
In [124]: df["grade"] = df["raw_grade"].astype("category") In [125]: df["grade"] Out[125]: 0 a 1 b 2 b 3 a 4 a 5 e Name: grade, dtype: category Categories (3, object): ['a', 'b', 'e']
Rename the categories to more meaningful names (assigning to Series.cat.categories() is in place!).
Series.cat.categories()
In [126]: df["grade"].cat.categories = ["very good", "good", "very bad"]
Reorder the categories and simultaneously add the missing categories (methods under Series.cat() return a new Series by default).
Series.cat()
In [127]: df["grade"] = df["grade"].cat.set_categories( .....: ["very bad", "bad", "medium", "good", "very good"] .....: ) .....: In [128]: df["grade"] Out[128]: 0 very good 1 good 2 good 3 very good 4 very good 5 very bad Name: grade, dtype: category Categories (5, object): ['very bad', 'bad', 'medium', 'good', 'very good']
Sorting is per order in the categories, not lexical order.
In [129]: df.sort_values(by="grade") Out[129]: id raw_grade grade 5 6 e very bad 1 2 b good 2 3 b good 0 1 a very good 3 4 a very good 4 5 a very good
Grouping by a categorical column also shows empty categories.
In [130]: df.groupby("grade").size() Out[130]: grade very bad 1 bad 0 medium 0 good 2 very good 3 dtype: int64
See the Plotting docs.
We use the standard convention for referencing the matplotlib API:
In [131]: import matplotlib.pyplot as plt In [132]: plt.close("all")
In [133]: ts = pd.Series(np.random.randn(1000), index=pd.date_range("1/1/2000", periods=1000)) In [134]: ts = ts.cumsum() In [135]: ts.plot() Out[135]: <AxesSubplot:>
On a DataFrame, the plot() method is a convenience to plot all of the columns with labels:
plot()
In [136]: df = pd.DataFrame( .....: np.random.randn(1000, 4), index=ts.index, columns=["A", "B", "C", "D"] .....: ) .....: In [137]: df = df.cumsum() In [138]: plt.figure() Out[138]: <Figure size 640x480 with 0 Axes> In [139]: df.plot() Out[139]: <AxesSubplot:> In [140]: plt.legend(loc='best') Out[140]: <matplotlib.legend.Legend at 0x7fa9468dc220>
Writing to a csv file.
In [141]: df.to_csv("foo.csv")
Reading from a csv file.
In [142]: pd.read_csv("foo.csv") Out[142]: Unnamed: 0 A B C D 0 2000-01-01 0.350262 0.843315 1.798556 0.782234 1 2000-01-02 -0.586873 0.034907 1.923792 -0.562651 2 2000-01-03 -1.245477 -0.963406 2.269575 -1.612566 3 2000-01-04 -0.252830 -0.498066 3.176886 -1.275581 4 2000-01-05 -1.044057 0.118042 2.768571 0.386039 .. ... ... ... ... ... 995 2002-09-22 -48.017654 31.474551 69.146374 -47.541670 996 2002-09-23 -47.207912 32.627390 68.505254 -48.828331 997 2002-09-24 -48.907133 31.990402 67.310924 -49.391051 998 2002-09-25 -50.146062 33.716770 67.717434 -49.037577 999 2002-09-26 -49.724318 33.479952 68.108014 -48.822030 [1000 rows x 5 columns]
Reading and writing to HDFStores.
Writing to a HDF5 Store.
In [143]: df.to_hdf("foo.h5", "df")
Reading from a HDF5 Store.
In [144]: pd.read_hdf("foo.h5", "df") Out[144]: A B C D 2000-01-01 0.350262 0.843315 1.798556 0.782234 2000-01-02 -0.586873 0.034907 1.923792 -0.562651 2000-01-03 -1.245477 -0.963406 2.269575 -1.612566 2000-01-04 -0.252830 -0.498066 3.176886 -1.275581 2000-01-05 -1.044057 0.118042 2.768571 0.386039 ... ... ... ... ... 2002-09-22 -48.017654 31.474551 69.146374 -47.541670 2002-09-23 -47.207912 32.627390 68.505254 -48.828331 2002-09-24 -48.907133 31.990402 67.310924 -49.391051 2002-09-25 -50.146062 33.716770 67.717434 -49.037577 2002-09-26 -49.724318 33.479952 68.108014 -48.822030 [1000 rows x 4 columns]
Reading and writing to MS Excel.
Writing to an excel file.
In [145]: df.to_excel("foo.xlsx", sheet_name="Sheet1")
Reading from an excel file.
In [146]: pd.read_excel("foo.xlsx", "Sheet1", index_col=None, na_values=["NA"]) Out[146]: Unnamed: 0 A B C D 0 2000-01-01 0.350262 0.843315 1.798556 0.782234 1 2000-01-02 -0.586873 0.034907 1.923792 -0.562651 2 2000-01-03 -1.245477 -0.963406 2.269575 -1.612566 3 2000-01-04 -0.252830 -0.498066 3.176886 -1.275581 4 2000-01-05 -1.044057 0.118042 2.768571 0.386039 .. ... ... ... ... ... 995 2002-09-22 -48.017654 31.474551 69.146374 -47.541670 996 2002-09-23 -47.207912 32.627390 68.505254 -48.828331 997 2002-09-24 -48.907133 31.990402 67.310924 -49.391051 998 2002-09-25 -50.146062 33.716770 67.717434 -49.037577 999 2002-09-26 -49.724318 33.479952 68.108014 -48.822030 [1000 rows x 5 columns]
If you are attempting to perform an operation you might see an exception like:
>>> if pd.Series([False, True, False]): ... print("I was true") Traceback ... ValueError: The truth value of an array is ambiguous. Use a.empty, a.any() or a.all().
See Comparisons for an explanation and what to do.
See Gotchas as well.