Cookbook — pandas 2.2.3 documentation (original) (raw)

This is a repository for short and sweet examples and links for useful pandas recipes. We encourage users to add to this documentation.

Adding interesting links and/or inline examples to this section is a great First Pull Request.

Simplified, condensed, new-user friendly, in-line examples have been inserted where possible to augment the Stack-Overflow and GitHub links. Many of the links contain expanded information, above what the in-line examples offer.

pandas (pd) and NumPy (np) are the only two abbreviated imported modules. The rest are kept explicitly imported for newer users.

Idioms#

These are some neat pandas idioms

if-then/if-then-else on one column, and assignment to another one or more columns:

In [1]: df = pd.DataFrame( ...: {"AAA": [4, 5, 6, 7], "BBB": [10, 20, 30, 40], "CCC": [100, 50, -30, -50]} ...: ) ...:

In [2]: df Out[2]: AAA BBB CCC 0 4 10 100 1 5 20 50 2 6 30 -30 3 7 40 -50

if-then…#

An if-then on one column

In [3]: df.loc[df.AAA >= 5, "BBB"] = -1

In [4]: df Out[4]: AAA BBB CCC 0 4 10 100 1 5 -1 50 2 6 -1 -30 3 7 -1 -50

An if-then with assignment to 2 columns:

In [5]: df.loc[df.AAA >= 5, ["BBB", "CCC"]] = 555

In [6]: df Out[6]: AAA BBB CCC 0 4 10 100 1 5 555 555 2 6 555 555 3 7 555 555

Add another line with different logic, to do the -else

In [7]: df.loc[df.AAA < 5, ["BBB", "CCC"]] = 2000

In [8]: df Out[8]: AAA BBB CCC 0 4 2000 2000 1 5 555 555 2 6 555 555 3 7 555 555

Or use pandas where after you’ve set up a mask

In [9]: df_mask = pd.DataFrame( ...: {"AAA": [True] * 4, "BBB": [False] * 4, "CCC": [True, False] * 2} ...: ) ...:

In [10]: df.where(df_mask, -1000) Out[10]: AAA BBB CCC 0 4 -1000 2000 1 5 -1000 -1000 2 6 -1000 555 3 7 -1000 -1000

if-then-else using NumPy’s where()

In [11]: df = pd.DataFrame( ....: {"AAA": [4, 5, 6, 7], "BBB": [10, 20, 30, 40], "CCC": [100, 50, -30, -50]} ....: ) ....:

In [12]: df Out[12]: AAA BBB CCC 0 4 10 100 1 5 20 50 2 6 30 -30 3 7 40 -50

In [13]: df["logic"] = np.where(df["AAA"] > 5, "high", "low")

In [14]: df Out[14]: AAA BBB CCC logic 0 4 10 100 low 1 5 20 50 low 2 6 30 -30 high 3 7 40 -50 high

Splitting#

Split a frame with a boolean criterion

In [15]: df = pd.DataFrame( ....: {"AAA": [4, 5, 6, 7], "BBB": [10, 20, 30, 40], "CCC": [100, 50, -30, -50]} ....: ) ....:

In [16]: df Out[16]: AAA BBB CCC 0 4 10 100 1 5 20 50 2 6 30 -30 3 7 40 -50

In [17]: df[df.AAA <= 5] Out[17]: AAA BBB CCC 0 4 10 100 1 5 20 50

In [18]: df[df.AAA > 5] Out[18]: AAA BBB CCC 2 6 30 -30 3 7 40 -50

Building criteria#

Select with multi-column criteria

In [19]: df = pd.DataFrame( ....: {"AAA": [4, 5, 6, 7], "BBB": [10, 20, 30, 40], "CCC": [100, 50, -30, -50]} ....: ) ....:

In [20]: df Out[20]: AAA BBB CCC 0 4 10 100 1 5 20 50 2 6 30 -30 3 7 40 -50

…and (without assignment returns a Series)

In [21]: df.loc[(df["BBB"] < 25) & (df["CCC"] >= -40), "AAA"] Out[21]: 0 4 1 5 Name: AAA, dtype: int64

…or (without assignment returns a Series)

In [22]: df.loc[(df["BBB"] > 25) | (df["CCC"] >= -40), "AAA"] Out[22]: 0 4 1 5 2 6 3 7 Name: AAA, dtype: int64

…or (with assignment modifies the DataFrame.)

In [23]: df.loc[(df["BBB"] > 25) | (df["CCC"] >= 75), "AAA"] = 999

In [24]: df Out[24]: AAA BBB CCC 0 999 10 100 1 5 20 50 2 999 30 -30 3 999 40 -50

Select rows with data closest to certain value using argsort

In [25]: df = pd.DataFrame( ....: {"AAA": [4, 5, 6, 7], "BBB": [10, 20, 30, 40], "CCC": [100, 50, -30, -50]} ....: ) ....:

In [26]: df Out[26]: AAA BBB CCC 0 4 10 100 1 5 20 50 2 6 30 -30 3 7 40 -50

In [27]: aValue = 43.0

In [28]: df.loc[(df.CCC - aValue).abs().argsort()] Out[28]: AAA BBB CCC 1 5 20 50 0 4 10 100 2 6 30 -30 3 7 40 -50

Dynamically reduce a list of criteria using a binary operators

In [29]: df = pd.DataFrame( ....: {"AAA": [4, 5, 6, 7], "BBB": [10, 20, 30, 40], "CCC": [100, 50, -30, -50]} ....: ) ....:

In [30]: df Out[30]: AAA BBB CCC 0 4 10 100 1 5 20 50 2 6 30 -30 3 7 40 -50

In [31]: Crit1 = df.AAA <= 5.5

In [32]: Crit2 = df.BBB == 10.0

In [33]: Crit3 = df.CCC > -40.0

One could hard code:

In [34]: AllCrit = Crit1 & Crit2 & Crit3

…Or it can be done with a list of dynamically built criteria

In [35]: import functools

In [36]: CritList = [Crit1, Crit2, Crit3]

In [37]: AllCrit = functools.reduce(lambda x, y: x & y, CritList)

In [38]: df[AllCrit] Out[38]: AAA BBB CCC 0 4 10 100

Selection#

Dataframes#

The indexing docs.

Using both row labels and value conditionals

In [39]: df = pd.DataFrame( ....: {"AAA": [4, 5, 6, 7], "BBB": [10, 20, 30, 40], "CCC": [100, 50, -30, -50]} ....: ) ....:

In [40]: df Out[40]: AAA BBB CCC 0 4 10 100 1 5 20 50 2 6 30 -30 3 7 40 -50

In [41]: df[(df.AAA <= 6) & (df.index.isin([0, 2, 4]))] Out[41]: AAA BBB CCC 0 4 10 100 2 6 30 -30

Use loc for label-oriented slicing and iloc positional slicing GH 2904

In [42]: df = pd.DataFrame( ....: {"AAA": [4, 5, 6, 7], "BBB": [10, 20, 30, 40], "CCC": [100, 50, -30, -50]}, ....: index=["foo", "bar", "boo", "kar"], ....: ) ....:

There are 2 explicit slicing methods, with a third general case

1. Positional-oriented (Python slicing style : exclusive of end)
2. Label-oriented (Non-Python slicing style : inclusive of end)
3. General (Either slicing style : depends on if the slice contains labels or positions)

In [43]: df.loc["bar":"kar"] # Label Out[43]: AAA BBB CCC bar 5 20 50 boo 6 30 -30 kar 7 40 -50

Generic

In [44]: df[0:3] Out[44]: AAA BBB CCC foo 4 10 100 bar 5 20 50 boo 6 30 -30

In [45]: df["bar":"kar"] Out[45]: AAA BBB CCC bar 5 20 50 boo 6 30 -30 kar 7 40 -50

Ambiguity arises when an index consists of integers with a non-zero start or non-unit increment.

In [46]: data = {"AAA": [4, 5, 6, 7], "BBB": [10, 20, 30, 40], "CCC": [100, 50, -30, -50]}

In [47]: df2 = pd.DataFrame(data=data, index=[1, 2, 3, 4]) # Note index starts at 1.

In [48]: df2.iloc[1:3] # Position-oriented Out[48]: AAA BBB CCC 2 5 20 50 3 6 30 -30

In [49]: df2.loc[1:3] # Label-oriented Out[49]: AAA BBB CCC 1 4 10 100 2 5 20 50 3 6 30 -30

Using inverse operator (~) to take the complement of a mask

In [50]: df = pd.DataFrame( ....: {"AAA": [4, 5, 6, 7], "BBB": [10, 20, 30, 40], "CCC": [100, 50, -30, -50]} ....: ) ....:

In [51]: df Out[51]: AAA BBB CCC 0 4 10 100 1 5 20 50 2 6 30 -30 3 7 40 -50

In [52]: df[~((df.AAA <= 6) & (df.index.isin([0, 2, 4])))] Out[52]: AAA BBB CCC 1 5 20 50 3 7 40 -50

New columns#

Efficiently and dynamically creating new columns using DataFrame.map (previously named applymap)

In [53]: df = pd.DataFrame({"AAA": [1, 2, 1, 3], "BBB": [1, 1, 2, 2], "CCC": [2, 1, 3, 1]})

In [54]: df Out[54]: AAA BBB CCC 0 1 1 2 1 2 1 1 2 1 2 3 3 3 2 1

In [55]: source_cols = df.columns # Or some subset would work too

In [56]: new_cols = [str(x) + "_cat" for x in source_cols]

In [57]: categories = {1: "Alpha", 2: "Beta", 3: "Charlie"}

In [58]: df[new_cols] = df[source_cols].map(categories.get)

In [59]: df Out[59]: AAA BBB CCC AAA_cat BBB_cat CCC_cat 0 1 1 2 Alpha Alpha Beta 1 2 1 1 Beta Alpha Alpha 2 1 2 3 Alpha Beta Charlie 3 3 2 1 Charlie Beta Alpha

Keep other columns when using min() with groupby

In [60]: df = pd.DataFrame( ....: {"AAA": [1, 1, 1, 2, 2, 2, 3, 3], "BBB": [2, 1, 3, 4, 5, 1, 2, 3]} ....: ) ....:

In [61]: df Out[61]: AAA BBB 0 1 2 1 1 1 2 1 3 3 2 4 4 2 5 5 2 1 6 3 2 7 3 3

Method 1 : idxmin() to get the index of the minimums

In [62]: df.loc[df.groupby("AAA")["BBB"].idxmin()] Out[62]: AAA BBB 1 1 1 5 2 1 6 3 2

Method 2 : sort then take first of each

In [63]: df.sort_values(by="BBB").groupby("AAA", as_index=False).first() Out[63]: AAA BBB 0 1 1 1 2 1 2 3 2

Notice the same results, with the exception of the index.

Multiindexing#

The multindexing docs.

Creating a MultiIndex from a labeled frame

In [64]: df = pd.DataFrame( ....: { ....: "row": [0, 1, 2], ....: "One_X": [1.1, 1.1, 1.1], ....: "One_Y": [1.2, 1.2, 1.2], ....: "Two_X": [1.11, 1.11, 1.11], ....: "Two_Y": [1.22, 1.22, 1.22], ....: } ....: ) ....:

In [65]: df Out[65]: row One_X One_Y Two_X Two_Y 0 0 1.1 1.2 1.11 1.22 1 1 1.1 1.2 1.11 1.22 2 2 1.1 1.2 1.11 1.22

As Labelled Index

In [66]: df = df.set_index("row")

In [67]: df Out[67]: One_X One_Y Two_X Two_Y row
0 1.1 1.2 1.11 1.22 1 1.1 1.2 1.11 1.22 2 1.1 1.2 1.11 1.22

With Hierarchical Columns

In [68]: df.columns = pd.MultiIndex.from_tuples([tuple(c.split("_")) for c in df.columns])

In [69]: df Out[69]: One Two
X Y X Y row
0 1.1 1.2 1.11 1.22 1 1.1 1.2 1.11 1.22 2 1.1 1.2 1.11 1.22

Now stack & Reset

In [70]: df = df.stack(0, future_stack=True).reset_index(1)

In [71]: df Out[71]: level_1 X Y row
0 One 1.10 1.20 0 Two 1.11 1.22 1 One 1.10 1.20 1 Two 1.11 1.22 2 One 1.10 1.20 2 Two 1.11 1.22

And fix the labels (Notice the label 'level_1' got added automatically)

In [72]: df.columns = ["Sample", "All_X", "All_Y"]

In [73]: df Out[73]: Sample All_X All_Y row
0 One 1.10 1.20 0 Two 1.11 1.22 1 One 1.10 1.20 1 Two 1.11 1.22 2 One 1.10 1.20 2 Two 1.11 1.22

Arithmetic#

Performing arithmetic with a MultiIndex that needs broadcasting

In [74]: cols = pd.MultiIndex.from_tuples( ....: [(x, y) for x in ["A", "B", "C"] for y in ["O", "I"]] ....: ) ....:

In [75]: df = pd.DataFrame(np.random.randn(2, 6), index=["n", "m"], columns=cols)

In [76]: df Out[76]: A B C
O I O I O I n 0.469112 -0.282863 -1.509059 -1.135632 1.212112 -0.173215 m 0.119209 -1.044236 -0.861849 -2.104569 -0.494929 1.071804

In [77]: df = df.div(df["C"], level=1)

In [78]: df Out[78]: A B C
O I O I O I n 0.387021 1.633022 -1.244983 6.556214 1.0 1.0 m -0.240860 -0.974279 1.741358 -1.963577 1.0 1.0

Slicing#

Slicing a MultiIndex with xs

In [79]: coords = [("AA", "one"), ("AA", "six"), ("BB", "one"), ("BB", "two"), ("BB", "six")]

In [80]: index = pd.MultiIndex.from_tuples(coords)

In [81]: df = pd.DataFrame([11, 22, 33, 44, 55], index, ["MyData"])

In [82]: df Out[82]: MyData AA one 11 six 22 BB one 33 two 44 six 55

To take the cross section of the 1st level and 1st axis the index:

Note : level and axis are optional, and default to zero

In [83]: df.xs("BB", level=0, axis=0) Out[83]: MyData one 33 two 44 six 55

…and now the 2nd level of the 1st axis.

In [84]: df.xs("six", level=1, axis=0) Out[84]: MyData AA 22 BB 55

Slicing a MultiIndex with xs, method #2

In [85]: import itertools

In [86]: index = list(itertools.product(["Ada", "Quinn", "Violet"], ["Comp", "Math", "Sci"]))

In [87]: headr = list(itertools.product(["Exams", "Labs"], ["I", "II"]))

In [88]: indx = pd.MultiIndex.from_tuples(index, names=["Student", "Course"])

In [89]: cols = pd.MultiIndex.from_tuples(headr) # Notice these are un-named

In [90]: data = [[70 + x + y + (x * y) % 3 for x in range(4)] for y in range(9)]

In [91]: df = pd.DataFrame(data, indx, cols)

In [92]: df Out[92]: Exams Labs
I II I II Student Course
Ada Comp 70 71 72 73 Math 71 73 75 74 Sci 72 75 75 75 Quinn Comp 73 74 75 76 Math 74 76 78 77 Sci 75 78 78 78 Violet Comp 76 77 78 79 Math 77 79 81 80 Sci 78 81 81 81

In [93]: All = slice(None)

In [94]: df.loc["Violet"] Out[94]: Exams Labs
I II I II Course
Comp 76 77 78 79 Math 77 79 81 80 Sci 78 81 81 81

In [95]: df.loc[(All, "Math"), All] Out[95]: Exams Labs
I II I II Student Course
Ada Math 71 73 75 74 Quinn Math 74 76 78 77 Violet Math 77 79 81 80

In [96]: df.loc[(slice("Ada", "Quinn"), "Math"), All] Out[96]: Exams Labs
I II I II Student Course
Ada Math 71 73 75 74 Quinn Math 74 76 78 77

In [97]: df.loc[(All, "Math"), ("Exams")] Out[97]: I II Student Course
Ada Math 71 73 Quinn Math 74 76 Violet Math 77 79

In [98]: df.loc[(All, "Math"), (All, "II")] Out[98]: Exams Labs II II Student Course
Ada Math 73 74 Quinn Math 76 77 Violet Math 79 80

Setting portions of a MultiIndex with xs

Sorting#

Sort by specific column or an ordered list of columns, with a MultiIndex

In [99]: df.sort_values(by=("Labs", "II"), ascending=False) Out[99]: Exams Labs
I II I II Student Course
Violet Sci 78 81 81 81 Math 77 79 81 80 Comp 76 77 78 79 Quinn Sci 75 78 78 78 Math 74 76 78 77 Comp 73 74 75 76 Ada Sci 72 75 75 75 Math 71 73 75 74 Comp 70 71 72 73

Partial selection, the need for sortedness GH 2995

Levels#

Prepending a level to a multiindex

Flatten Hierarchical columns

Missing data#

The missing data docs.

Fill forward a reversed timeseries

In [100]: df = pd.DataFrame( .....: np.random.randn(6, 1), .....: index=pd.date_range("2013-08-01", periods=6, freq="B"), .....: columns=list("A"), .....: ) .....:

In [101]: df.loc[df.index[3], "A"] = np.nan

In [102]: df Out[102]: A 2013-08-01 0.721555 2013-08-02 -0.706771 2013-08-05 -1.039575 2013-08-06 NaN 2013-08-07 -0.424972 2013-08-08 0.567020

In [103]: df.bfill() Out[103]: A 2013-08-01 0.721555 2013-08-02 -0.706771 2013-08-05 -1.039575 2013-08-06 -0.424972 2013-08-07 -0.424972 2013-08-08 0.567020

cumsum reset at NaN values

Replace#

Using replace with backrefs

Grouping#

The grouping docs.

Basic grouping with apply

Unlike agg, apply’s callable is passed a sub-DataFrame which gives you access to all the columns

In [104]: df = pd.DataFrame( .....: { .....: "animal": "cat dog cat fish dog cat cat".split(), .....: "size": list("SSMMMLL"), .....: "weight": [8, 10, 11, 1, 20, 12, 12], .....: "adult": [False] * 5 + [True] * 2, .....: } .....: ) .....:

In [105]: df Out[105]: animal size weight adult 0 cat S 8 False 1 dog S 10 False 2 cat M 11 False 3 fish M 1 False 4 dog M 20 False 5 cat L 12 True 6 cat L 12 True

List the size of the animals with the highest weight.

In [106]: df.groupby("animal").apply(lambda subf: subf["size"][subf["weight"].idxmax()], include_groups=False) Out[106]: animal cat L dog M fish M dtype: object

Using get_group

In [107]: gb = df.groupby("animal")

In [108]: gb.get_group("cat") Out[108]: animal size weight adult 0 cat S 8 False 2 cat M 11 False 5 cat L 12 True 6 cat L 12 True

Apply to different items in a group

In [109]: def GrowUp(x): .....: avg_weight = sum(x[x["size"] == "S"].weight * 1.5) .....: avg_weight += sum(x[x["size"] == "M"].weight * 1.25) .....: avg_weight += sum(x[x["size"] == "L"].weight) .....: avg_weight /= len(x) .....: return pd.Series(["L", avg_weight, True], index=["size", "weight", "adult"]) .....:

In [110]: expected_df = gb.apply(GrowUp, include_groups=False)

In [111]: expected_df Out[111]: size weight adult animal
cat L 12.4375 True dog L 20.0000 True fish L 1.2500 True

Expanding apply

In [112]: S = pd.Series([i / 100.0 for i in range(1, 11)])

In [113]: def cum_ret(x, y): .....: return x * (1 + y) .....:

In [114]: def red(x): .....: return functools.reduce(cum_ret, x, 1.0) .....:

In [115]: S.expanding().apply(red, raw=True) Out[115]: 0 1.010000 1 1.030200 2 1.061106 3 1.103550 4 1.158728 5 1.228251 6 1.314229 7 1.419367 8 1.547110 9 1.701821 dtype: float64

Replacing some values with mean of the rest of a group

In [116]: df = pd.DataFrame({"A": [1, 1, 2, 2], "B": [1, -1, 1, 2]})

In [117]: gb = df.groupby("A")

In [118]: def replace(g): .....: mask = g < 0 .....: return g.where(mask, g[mask].mean()) .....:

In [119]: gb.transform(replace) Out[119]: B 0 1 1 1 2 1 3 2

Sort groups by aggregated data

In [120]: df = pd.DataFrame( .....: { .....: "code": ["foo", "bar", "baz"] * 2, .....: "data": [0.16, -0.21, 0.33, 0.45, -0.59, 0.62], .....: "flag": [False, True] * 3, .....: } .....: ) .....:

In [121]: code_groups = df.groupby("code")

In [122]: agg_n_sort_order = code_groups[["data"]].transform("sum").sort_values(by="data")

In [123]: sorted_df = df.loc[agg_n_sort_order.index]

In [124]: sorted_df Out[124]: code data flag 1 bar -0.21 True 4 bar -0.59 False 0 foo 0.16 False 3 foo 0.45 True 2 baz 0.33 False 5 baz 0.62 True

Create multiple aggregated columns

In [125]: rng = pd.date_range(start="2014-10-07", periods=10, freq="2min")

In [126]: ts = pd.Series(data=list(range(10)), index=rng)

In [127]: def MyCust(x): .....: if len(x) > 2: .....: return x.iloc[1] * 1.234 .....: return pd.NaT .....:

In [128]: mhc = {"Mean": "mean", "Max": "max", "Custom": MyCust}

In [129]: ts.resample("5min").apply(mhc) Out[129]: Mean Max Custom 2014-10-07 00:00:00 1.0 2 1.234 2014-10-07 00:05:00 3.5 4 NaT 2014-10-07 00:10:00 6.0 7 7.404 2014-10-07 00:15:00 8.5 9 NaT

In [130]: ts Out[130]: 2014-10-07 00:00:00 0 2014-10-07 00:02:00 1 2014-10-07 00:04:00 2 2014-10-07 00:06:00 3 2014-10-07 00:08:00 4 2014-10-07 00:10:00 5 2014-10-07 00:12:00 6 2014-10-07 00:14:00 7 2014-10-07 00:16:00 8 2014-10-07 00🔞00 9 Freq: 2min, dtype: int64

Create a value counts column and reassign back to the DataFrame

In [131]: df = pd.DataFrame( .....: {"Color": "Red Red Red Blue".split(), "Value": [100, 150, 50, 50]} .....: ) .....:

In [132]: df Out[132]: Color Value 0 Red 100 1 Red 150 2 Red 50 3 Blue 50

In [133]: df["Counts"] = df.groupby(["Color"]).transform(len)

In [134]: df Out[134]: Color Value Counts 0 Red 100 3 1 Red 150 3 2 Red 50 3 3 Blue 50 1

Shift groups of the values in a column based on the index

In [135]: df = pd.DataFrame( .....: {"line_race": [10, 10, 8, 10, 10, 8], "beyer": [99, 102, 103, 103, 88, 100]}, .....: index=[ .....: "Last Gunfighter", .....: "Last Gunfighter", .....: "Last Gunfighter", .....: "Paynter", .....: "Paynter", .....: "Paynter", .....: ], .....: ) .....:

In [136]: df Out[136]: line_race beyer Last Gunfighter 10 99 Last Gunfighter 10 102 Last Gunfighter 8 103 Paynter 10 103 Paynter 10 88 Paynter 8 100

In [137]: df["beyer_shifted"] = df.groupby(level=0)["beyer"].shift(1)

In [138]: df Out[138]: line_race beyer beyer_shifted Last Gunfighter 10 99 NaN Last Gunfighter 10 102 99.0 Last Gunfighter 8 103 102.0 Paynter 10 103 NaN Paynter 10 88 103.0 Paynter 8 100 88.0

Select row with maximum value from each group

In [139]: df = pd.DataFrame( .....: { .....: "host": ["other", "other", "that", "this", "this"], .....: "service": ["mail", "web", "mail", "mail", "web"], .....: "no": [1, 2, 1, 2, 1], .....: } .....: ).set_index(["host", "service"]) .....:

In [140]: mask = df.groupby(level=0).agg("idxmax")

In [141]: df_count = df.loc[mask["no"]].reset_index()

In [142]: df_count Out[142]: host service no 0 other web 2 1 that mail 1 2 this mail 2

Grouping like Python’s itertools.groupby

In [143]: df = pd.DataFrame([0, 1, 0, 1, 1, 1, 0, 1, 1], columns=["A"])

In [144]: df["A"].groupby((df["A"] != df["A"].shift()).cumsum()).groups Out[144]: {1: [0], 2: [1], 3: [2], 4: [3, 4, 5], 5: [6], 6: [7, 8]}

In [145]: df["A"].groupby((df["A"] != df["A"].shift()).cumsum()).cumsum() Out[145]: 0 0 1 1 2 0 3 1 4 2 5 3 6 0 7 1 8 2 Name: A, dtype: int64

Expanding data#

Alignment and to-date

Rolling Computation window based on values instead of counts

Rolling Mean by Time Interval

Splitting#

Splitting a frame

Create a list of dataframes, split using a delineation based on logic included in rows.

In [146]: df = pd.DataFrame( .....: data={ .....: "Case": ["A", "A", "A", "B", "A", "A", "B", "A", "A"], .....: "Data": np.random.randn(9), .....: } .....: ) .....:

In [147]: dfs = list( .....: zip( .....: *df.groupby( .....: (1 * (df["Case"] == "B")) .....: .cumsum() .....: .rolling(window=3, min_periods=1) .....: .median() .....: ) .....: ) .....: )[-1] .....:

In [148]: dfs[0] Out[148]: Case Data 0 A 0.276232 1 A -1.087401 2 A -0.673690 3 B 0.113648

In [149]: dfs[1] Out[149]: Case Data 4 A -1.478427 5 A 0.524988 6 B 0.404705

In [150]: dfs[2] Out[150]: Case Data 7 A 0.577046 8 A -1.715002

Pivot#

The Pivot docs.

Partial sums and subtotals

In [151]: df = pd.DataFrame( .....: data={ .....: "Province": ["ON", "QC", "BC", "AL", "AL", "MN", "ON"], .....: "City": [ .....: "Toronto", .....: "Montreal", .....: "Vancouver", .....: "Calgary", .....: "Edmonton", .....: "Winnipeg", .....: "Windsor", .....: ], .....: "Sales": [13, 6, 16, 8, 4, 3, 1], .....: } .....: ) .....:

In [152]: table = pd.pivot_table( .....: df, .....: values=["Sales"], .....: index=["Province"], .....: columns=["City"], .....: aggfunc="sum", .....: margins=True, .....: ) .....:

In [153]: table.stack("City", future_stack=True) Out[153]: Sales Province City
AL Calgary 8.0 Edmonton 4.0 Montreal NaN Toronto NaN Vancouver NaN ... ... All Toronto 13.0 Vancouver 16.0 Windsor 1.0 Winnipeg 3.0 All 51.0

[48 rows x 1 columns]

Frequency table like plyr in R

In [154]: grades = [48, 99, 75, 80, 42, 80, 72, 68, 36, 78]

In [155]: df = pd.DataFrame( .....: { .....: "ID": ["x%d" % r for r in range(10)], .....: "Gender": ["F", "M", "F", "M", "F", "M", "F", "M", "M", "M"], .....: "ExamYear": [ .....: "2007", .....: "2007", .....: "2007", .....: "2008", .....: "2008", .....: "2008", .....: "2008", .....: "2009", .....: "2009", .....: "2009", .....: ], .....: "Class": [ .....: "algebra", .....: "stats", .....: "bio", .....: "algebra", .....: "algebra", .....: "stats", .....: "stats", .....: "algebra", .....: "bio", .....: "bio", .....: ], .....: "Participated": [ .....: "yes", .....: "yes", .....: "yes", .....: "yes", .....: "no", .....: "yes", .....: "yes", .....: "yes", .....: "yes", .....: "yes", .....: ], .....: "Passed": ["yes" if x > 50 else "no" for x in grades], .....: "Employed": [ .....: True, .....: True, .....: True, .....: False, .....: False, .....: False, .....: False, .....: True, .....: True, .....: False, .....: ], .....: "Grade": grades, .....: } .....: ) .....:

In [156]: df.groupby("ExamYear").agg( .....: { .....: "Participated": lambda x: x.value_counts()["yes"], .....: "Passed": lambda x: sum(x == "yes"), .....: "Employed": lambda x: sum(x), .....: "Grade": lambda x: sum(x) / len(x), .....: } .....: ) .....: Out[156]: Participated Passed Employed Grade ExamYear
2007 3 2 3 74.000000 2008 3 3 0 68.500000 2009 3 2 2 60.666667

Plot pandas DataFrame with year over year data

To create year and month cross tabulation:

In [157]: df = pd.DataFrame( .....: {"value": np.random.randn(36)}, .....: index=pd.date_range("2011-01-01", freq="ME", periods=36), .....: ) .....:

In [158]: pd.pivot_table( .....: df, index=df.index.month, columns=df.index.year, values="value", aggfunc="sum" .....: ) .....: Out[158]: 2011 2012 2013 1 -1.039268 -0.968914 2.565646 2 -0.370647 -1.294524 1.431256 3 -1.157892 0.413738 1.340309 4 -1.344312 0.276662 -1.170299 5 0.844885 -0.472035 -0.226169 6 1.075770 -0.013960 0.410835 7 -0.109050 -0.362543 0.813850 8 1.643563 -0.006154 0.132003 9 -1.469388 -0.923061 -0.827317 10 0.357021 0.895717 -0.076467 11 -0.674600 0.805244 -1.187678 12 -1.776904 -1.206412 1.130127

Apply#

Rolling apply to organize - Turning embedded lists into a MultiIndex frame

In [159]: df = pd.DataFrame( .....: data={ .....: "A": [[2, 4, 8, 16], [100, 200], [10, 20, 30]], .....: "B": [["a", "b", "c"], ["jj", "kk"], ["ccc"]], .....: }, .....: index=["I", "II", "III"], .....: ) .....:

In [160]: def SeriesFromSubList(aList): .....: return pd.Series(aList) .....:

In [161]: df_orgz = pd.concat( .....: {ind: row.apply(SeriesFromSubList) for ind, row in df.iterrows()} .....: ) .....:

In [162]: df_orgz Out[162]: 0 1 2 3 I A 2 4 8 16.0 B a b c NaN II A 100 200 NaN NaN B jj kk NaN NaN III A 10 20.0 30.0 NaN B ccc NaN NaN NaN

Rolling apply with a DataFrame returning a Series

Rolling Apply to multiple columns where function calculates a Series before a Scalar from the Series is returned

In [163]: df = pd.DataFrame( .....: data=np.random.randn(2000, 2) / 10000, .....: index=pd.date_range("2001-01-01", periods=2000), .....: columns=["A", "B"], .....: ) .....:

In [164]: df Out[164]: A B 2001-01-01 -0.000144 -0.000141 2001-01-02 0.000161 0.000102 2001-01-03 0.000057 0.000088 2001-01-04 -0.000221 0.000097 2001-01-05 -0.000201 -0.000041 ... ... ... 2006-06-19 0.000040 -0.000235 2006-06-20 -0.000123 -0.000021 2006-06-21 -0.000113 0.000114 2006-06-22 0.000136 0.000109 2006-06-23 0.000027 0.000030

[2000 rows x 2 columns]

In [165]: def gm(df, const): .....: v = ((((df["A"] + df["B"]) + 1).cumprod()) - 1) * const .....: return v.iloc[-1] .....:

In [166]: s = pd.Series( .....: { .....: df.index[i]: gm(df.iloc[i: min(i + 51, len(df) - 1)], 5) .....: for i in range(len(df) - 50) .....: } .....: ) .....:

In [167]: s Out[167]: 2001-01-01 0.000930 2001-01-02 0.002615 2001-01-03 0.001281 2001-01-04 0.001117 2001-01-05 0.002772 ...
2006-04-30 0.003296 2006-05-01 0.002629 2006-05-02 0.002081 2006-05-03 0.004247 2006-05-04 0.003928 Length: 1950, dtype: float64

Rolling apply with a DataFrame returning a Scalar

Rolling Apply to multiple columns where function returns a Scalar (Volume Weighted Average Price)

In [168]: rng = pd.date_range(start="2014-01-01", periods=100)

In [169]: df = pd.DataFrame( .....: { .....: "Open": np.random.randn(len(rng)), .....: "Close": np.random.randn(len(rng)), .....: "Volume": np.random.randint(100, 2000, len(rng)), .....: }, .....: index=rng, .....: ) .....:

In [170]: df Out[170]: Open Close Volume 2014-01-01 -1.611353 -0.492885 1219 2014-01-02 -3.000951 0.445794 1054 2014-01-03 -0.138359 -0.076081 1381 2014-01-04 0.301568 1.198259 1253 2014-01-05 0.276381 -0.669831 1728 ... ... ... ... 2014-04-06 -0.040338 0.937843 1188 2014-04-07 0.359661 -0.285908 1864 2014-04-08 0.060978 1.714814 941 2014-04-09 1.759055 -0.455942 1065 2014-04-10 0.138185 -1.147008 1453

[100 rows x 3 columns]

In [171]: def vwap(bars): .....: return (bars.Close * bars.Volume).sum() / bars.Volume.sum() .....:

In [172]: window = 5

In [173]: s = pd.concat( .....: [ .....: (pd.Series(vwap(df.iloc[i: i + window]), index=[df.index[i + window]])) .....: for i in range(len(df) - window) .....: ] .....: ) .....:

In [174]: s.round(2) Out[174]: 2014-01-06 0.02 2014-01-07 0.11 2014-01-08 0.10 2014-01-09 0.07 2014-01-10 -0.29 ... 2014-04-06 -0.63 2014-04-07 -0.02 2014-04-08 -0.03 2014-04-09 0.34 2014-04-10 0.29 Length: 95, dtype: float64

Timeseries#

Between times

Using indexer between time

Constructing a datetime range that excludes weekends and includes only certain times

Vectorized Lookup

Aggregation and plotting time series

Turn a matrix with hours in columns and days in rows into a continuous row sequence in the form of a time series.How to rearrange a Python pandas DataFrame?

Dealing with duplicates when reindexing a timeseries to a specified frequency

Calculate the first day of the month for each entry in a DatetimeIndex

In [175]: dates = pd.date_range("2000-01-01", periods=5)

In [176]: dates.to_period(freq="M").to_timestamp() Out[176]: DatetimeIndex(['2000-01-01', '2000-01-01', '2000-01-01', '2000-01-01', '2000-01-01'], dtype='datetime64[ns]', freq=None)

Resampling#

The Resample docs.

Using Grouper instead of TimeGrouper for time grouping of values

Time grouping with some missing values

Valid frequency arguments to Grouper Timeseries

Grouping using a MultiIndex

Using TimeGrouper and another grouping to create subgroups, then apply a custom function GH 3791

Resampling with custom periods

Resample intraday frame without adding new days

Resample minute data

Resample with groupby

Merge#

The Join docs.

Concatenate two dataframes with overlapping index (emulate R rbind)

In [177]: rng = pd.date_range("2000-01-01", periods=6)

In [178]: df1 = pd.DataFrame(np.random.randn(6, 3), index=rng, columns=["A", "B", "C"])

In [179]: df2 = df1.copy()

Depending on df construction, ignore_index may be needed

In [180]: df = pd.concat([df1, df2], ignore_index=True)

In [181]: df Out[181]: A B C 0 -0.870117 -0.479265 -0.790855 1 0.144817 1.726395 -0.464535 2 -0.821906 1.597605 0.187307 3 -0.128342 -1.511638 -0.289858 4 0.399194 -1.430030 -0.639760 5 1.115116 -2.012600 1.810662 6 -0.870117 -0.479265 -0.790855 7 0.144817 1.726395 -0.464535 8 -0.821906 1.597605 0.187307 9 -0.128342 -1.511638 -0.289858 10 0.399194 -1.430030 -0.639760 11 1.115116 -2.012600 1.810662

Self Join of a DataFrame GH 2996

In [182]: df = pd.DataFrame( .....: data={ .....: "Area": ["A"] * 5 + ["C"] * 2, .....: "Bins": [110] * 2 + [160] * 3 + [40] * 2, .....: "Test_0": [0, 1, 0, 1, 2, 0, 1], .....: "Data": np.random.randn(7), .....: } .....: ) .....:

In [183]: df Out[183]: Area Bins Test_0 Data 0 A 110 0 -0.433937 1 A 110 1 -0.160552 2 A 160 0 0.744434 3 A 160 1 1.754213 4 A 160 2 0.000850 5 C 40 0 0.342243 6 C 40 1 1.070599

In [184]: df["Test_1"] = df["Test_0"] - 1

In [185]: pd.merge( .....: df, .....: df, .....: left_on=["Bins", "Area", "Test_0"], .....: right_on=["Bins", "Area", "Test_1"], .....: suffixes=("_L", "_R"), .....: ) .....: Out[185]: Area Bins Test_0_L Data_L Test_1_L Test_0_R Data_R Test_1_R 0 A 110 0 -0.433937 -1 1 -0.160552 0 1 A 160 0 0.744434 -1 1 1.754213 0 2 A 160 1 1.754213 0 2 0.000850 1 3 C 40 0 0.342243 -1 1 1.070599 0

How to set the index and join

KDB like asof join

Join with a criteria based on the values

Using searchsorted to merge based on values inside a range

Plotting#

The Plotting docs.

Make Matplotlib look like R

Setting x-axis major and minor labels

Plotting multiple charts in an IPython Jupyter notebook

Creating a multi-line plot

Plotting a heatmap

Annotate a time-series plot

Annotate a time-series plot #2

Generate Embedded plots in excel files using Pandas, Vincent and xlsxwriter

Boxplot for each quartile of a stratifying variable

In [186]: df = pd.DataFrame( .....: { .....: "stratifying_var": np.random.uniform(0, 100, 20), .....: "price": np.random.normal(100, 5, 20), .....: } .....: ) .....:

In [187]: df["quartiles"] = pd.qcut( .....: df["stratifying_var"], 4, labels=["0-25%", "25-50%", "50-75%", "75-100%"] .....: ) .....:

In [188]: df.boxplot(column="price", by="quartiles") Out[188]: <Axes: title={'center': 'price'}, xlabel='quartiles'>

Data in/out#

Performance comparison of SQL vs HDF5

CSV#

The CSV docs

read_csv in action

appending to a csv

Reading a csv chunk-by-chunk

Reading only certain rows of a csv chunk-by-chunk

Reading the first few lines of a frame

Reading a file that is compressed but not by gzip/bz2 (the native compressed formats which read_csv understands). This example shows a WinZipped file, but is a general application of opening the file within a context manager and using that handle to read.See here

Inferring dtypes from a file

Dealing with bad lines GH 2886

Write a multi-row index CSV without writing duplicates

Reading multiple files to create a single DataFrame#

The best way to combine multiple files into a single DataFrame is to read the individual frames one by one, put all of the individual frames into a list, and then combine the frames in the list using pd.concat():

In [189]: for i in range(3): .....: data = pd.DataFrame(np.random.randn(10, 4)) .....: data.to_csv("file_{}.csv".format(i)) .....:

In [190]: files = ["file_0.csv", "file_1.csv", "file_2.csv"]

In [191]: result = pd.concat([pd.read_csv(f) for f in files], ignore_index=True)

You can use the same approach to read all files matching a pattern. Here is an example using glob:

In [192]: import glob

In [193]: import os

In [194]: files = glob.glob("file_*.csv")

In [195]: result = pd.concat([pd.read_csv(f) for f in files], ignore_index=True)

Finally, this strategy will work with the other pd.read_*(...) functions described in the io docs.

Parsing date components in multi-columns#

Parsing date components in multi-columns is faster with a format

In [196]: i = pd.date_range("20000101", periods=10000)

In [197]: df = pd.DataFrame({"year": i.year, "month": i.month, "day": i.day})

In [198]: df.head() Out[198]: year month day 0 2000 1 1 1 2000 1 2 2 2000 1 3 3 2000 1 4 4 2000 1 5

In [199]: %timeit pd.to_datetime(df.year * 10000 + df.month * 100 + df.day, format='%Y%m%d') .....: ds = df.apply(lambda x: "%04d%02d%02d" % (x["year"], x["month"], x["day"]), axis=1) .....: ds.head() .....: %timeit pd.to_datetime(ds) .....: 2.7 ms +- 240 us per loop (mean +- std. dev. of 7 runs, 100 loops each) 1.09 ms +- 5.62 us per loop (mean +- std. dev. of 7 runs, 1,000 loops each)

Skip row between header and data#

In [200]: data = """;;;; .....: ;;;; .....: ;;;; .....: ;;;; .....: ;;;; .....: ;;;; .....: ;;;; .....: ;;;; .....: ;;;; .....: ;;;; .....: date;Param1;Param2;Param4;Param5 .....: ;m²;°C;m²;m .....: ;;;; .....: 01.01.1990 00:00;1;1;2;3 .....: 01.01.1990 01:00;5;3;4;5 .....: 01.01.1990 02:00;9;5;6;7 .....: 01.01.1990 03:00;13;7;8;9 .....: 01.01.1990 04:00;17;9;10;11 .....: 01.01.1990 05:00;21;11;12;13 .....: """ .....:

Option 1: pass rows explicitly to skip rows#

In [201]: from io import StringIO

In [202]: pd.read_csv( .....: StringIO(data), .....: sep=";", .....: skiprows=[11, 12], .....: index_col=0, .....: parse_dates=True, .....: header=10, .....: ) .....: Out[202]: Param1 Param2 Param4 Param5 date
1990-01-01 00:00:00 1 1 2 3 1990-01-01 01:00:00 5 3 4 5 1990-01-01 02:00:00 9 5 6 7 1990-01-01 03:00:00 13 7 8 9 1990-01-01 04:00:00 17 9 10 11 1990-01-01 05:00:00 21 11 12 13

Option 2: read column names and then data#

In [203]: pd.read_csv(StringIO(data), sep=";", header=10, nrows=10).columns Out[203]: Index(['date', 'Param1', 'Param2', 'Param4', 'Param5'], dtype='object')

In [204]: columns = pd.read_csv(StringIO(data), sep=";", header=10, nrows=10).columns

In [205]: pd.read_csv( .....: StringIO(data), sep=";", index_col=0, header=12, parse_dates=True, names=columns .....: ) .....: Out[205]: Param1 Param2 Param4 Param5 date
1990-01-01 00:00:00 1 1 2 3 1990-01-01 01:00:00 5 3 4 5 1990-01-01 02:00:00 9 5 6 7 1990-01-01 03:00:00 13 7 8 9 1990-01-01 04:00:00 17 9 10 11 1990-01-01 05:00:00 21 11 12 13

SQL#

The SQL docs

Reading from databases with SQL

Excel#

The Excel docs

Reading from a filelike handle

Modifying formatting in XlsxWriter output

Loading only visible sheets GH 19842#issuecomment-892150745

HTML#

Reading HTML tables from a server that cannot handle the default request header

HDFStore#

The HDFStores docs

Simple queries with a Timestamp Index

Managing heterogeneous data using a linked multiple table hierarchy GH 3032

Merging on-disk tables with millions of rows

Avoiding inconsistencies when writing to a store from multiple processes/threads

De-duplicating a large store by chunks, essentially a recursive reduction operation. Shows a function for taking in data from csv file and creating a store by chunks, with date parsing as well.See here

Creating a store chunk-by-chunk from a csv file

Appending to a store, while creating a unique index

Large Data work flows

Reading in a sequence of files, then providing a global unique index to a store while appending

Groupby on a HDFStore with low group density

Groupby on a HDFStore with high group density

Hierarchical queries on a HDFStore

Counting with a HDFStore

Troubleshoot HDFStore exceptions

Setting min_itemsize with strings

Using ptrepack to create a completely-sorted-index on a store

Storing Attributes to a group node

In [206]: df = pd.DataFrame(np.random.randn(8, 3))

In [207]: store = pd.HDFStore("test.h5")

In [208]: store.put("df", df)

you can store an arbitrary Python object via pickle

In [209]: store.get_storer("df").attrs.my_attribute = {"A": 10}

In [210]: store.get_storer("df").attrs.my_attribute Out[210]: {'A': 10}

You can create or load a HDFStore in-memory by passing the driverparameter to PyTables. Changes are only written to disk when the HDFStore is closed.

In [211]: store = pd.HDFStore("test.h5", "w", driver="H5FD_CORE")

In [212]: df = pd.DataFrame(np.random.randn(8, 3))

In [213]: store["test"] = df

only after closing the store, data is written to disk:

In [214]: store.close()

Binary files#

pandas readily accepts NumPy record arrays, if you need to read in a binary file consisting of an array of C structs. For example, given this C program in a file called main.c compiled with gcc main.c -std=gnu99 on a 64-bit machine,

#include <stdio.h> #include <stdint.h>

typedef struct _Data { int32_t count; double avg; float scale; } Data;

int main(int argc, const char *argv[]) { size_t n = 10; Data d[n];

for (int i = 0; i < n; ++i)
{
    d[i].count = i;
    d[i].avg = i + 1.0;
    d[i].scale = (float) i + 2.0f;
}

FILE *file = fopen("binary.dat", "wb");
fwrite(&d, sizeof(Data), n, file);
fclose(file);

return 0;

}

the following Python code will read the binary file 'binary.dat' into a pandas DataFrame, where each element of the struct corresponds to a column in the frame:

names = "count", "avg", "scale"

note that the offsets are larger than the size of the type because of

struct padding

offsets = 0, 8, 16 formats = "i4", "f8", "f4" dt = np.dtype({"names": names, "offsets": offsets, "formats": formats}, align=True) df = pd.DataFrame(np.fromfile("binary.dat", dt))

Note

The offsets of the structure elements may be different depending on the architecture of the machine on which the file was created. Using a raw binary file format like this for general data storage is not recommended, as it is not cross platform. We recommended either HDF5 or parquet, both of which are supported by pandas’ IO facilities.

Computation#

Numerical integration (sample-based) of a time series

Correlation#

Often it’s useful to obtain the lower (or upper) triangular form of a correlation matrix calculated from DataFrame.corr(). This can be achieved by passing a boolean mask to where as follows:

In [215]: df = pd.DataFrame(np.random.random(size=(100, 5)))

In [216]: corr_mat = df.corr()

In [217]: mask = np.tril(np.ones_like(corr_mat, dtype=np.bool_), k=-1)

In [218]: corr_mat.where(mask) Out[218]: 0 1 2 3 4 0 NaN NaN NaN NaN NaN 1 -0.079861 NaN NaN NaN NaN 2 -0.236573 0.183801 NaN NaN NaN 3 -0.013795 -0.051975 0.037235 NaN NaN 4 -0.031974 0.118342 -0.073499 -0.02063 NaN

The method argument within DataFrame.corr can accept a callable in addition to the named correlation types. Here we compute the distance correlation matrix for a DataFrame object.

In [219]: def distcorr(x, y): .....: n = len(x) .....: a = np.zeros(shape=(n, n)) .....: b = np.zeros(shape=(n, n)) .....: for i in range(n): .....: for j in range(i + 1, n): .....: a[i, j] = abs(x[i] - x[j]) .....: b[i, j] = abs(y[i] - y[j]) .....: a += a.T .....: b += b.T .....: a_bar = np.vstack([np.nanmean(a, axis=0)] * n) .....: b_bar = np.vstack([np.nanmean(b, axis=0)] * n) .....: A = a - a_bar - a_bar.T + np.full(shape=(n, n), fill_value=a_bar.mean()) .....: B = b - b_bar - b_bar.T + np.full(shape=(n, n), fill_value=b_bar.mean()) .....: cov_ab = np.sqrt(np.nansum(A * B)) / n .....: std_a = np.sqrt(np.sqrt(np.nansum(A ** 2)) / n) .....: std_b = np.sqrt(np.sqrt(np.nansum(B ** 2)) / n) .....: return cov_ab / std_a / std_b .....:

In [220]: df = pd.DataFrame(np.random.normal(size=(100, 3)))

In [221]: df.corr(method=distcorr) Out[221]: 0 1 2 0 1.000000 0.197613 0.216328 1 0.197613 1.000000 0.208749 2 0.216328 0.208749 1.000000

Timedeltas#

The Timedeltas docs.

Using timedeltas

In [222]: import datetime

In [223]: s = pd.Series(pd.date_range("2012-1-1", periods=3, freq="D"))

In [224]: s - s.max() Out[224]: 0 -2 days 1 -1 days 2 0 days dtype: timedelta64[ns]

In [225]: s.max() - s Out[225]: 0 2 days 1 1 days 2 0 days dtype: timedelta64[ns]

In [226]: s - datetime.datetime(2011, 1, 1, 3, 5) Out[226]: 0 364 days 20:55:00 1 365 days 20:55:00 2 366 days 20:55:00 dtype: timedelta64[ns]

In [227]: s + datetime.timedelta(minutes=5) Out[227]: 0 2012-01-01 00:05:00 1 2012-01-02 00:05:00 2 2012-01-03 00:05:00 dtype: datetime64[ns]

In [228]: datetime.datetime(2011, 1, 1, 3, 5) - s Out[228]: 0 -365 days +03:05:00 1 -366 days +03:05:00 2 -367 days +03:05:00 dtype: timedelta64[ns]

In [229]: datetime.timedelta(minutes=5) + s Out[229]: 0 2012-01-01 00:05:00 1 2012-01-02 00:05:00 2 2012-01-03 00:05:00 dtype: datetime64[ns]

Adding and subtracting deltas and dates

In [230]: deltas = pd.Series([datetime.timedelta(days=i) for i in range(3)])

In [231]: df = pd.DataFrame({"A": s, "B": deltas})

In [232]: df Out[232]: A B 0 2012-01-01 0 days 1 2012-01-02 1 days 2 2012-01-03 2 days

In [233]: df["New Dates"] = df["A"] + df["B"]

In [234]: df["Delta"] = df["A"] - df["New Dates"]

In [235]: df Out[235]: A B New Dates Delta 0 2012-01-01 0 days 2012-01-01 0 days 1 2012-01-02 1 days 2012-01-03 -1 days 2 2012-01-03 2 days 2012-01-05 -2 days

In [236]: df.dtypes Out[236]: A datetime64[ns] B timedelta64[ns] New Dates datetime64[ns] Delta timedelta64[ns] dtype: object

Another example

Values can be set to NaT using np.nan, similar to datetime

In [237]: y = s - s.shift()

In [238]: y Out[238]: 0 NaT 1 1 days 2 1 days dtype: timedelta64[ns]

In [239]: y[1] = np.nan

In [240]: y Out[240]: 0 NaT 1 NaT 2 1 days dtype: timedelta64[ns]

Creating example data#

To create a dataframe from every combination of some given values, like R’s expand.grid()function, we can create a dict where the keys are column names and the values are lists of the data values:

In [241]: def expand_grid(data_dict): .....: rows = itertools.product(*data_dict.values()) .....: return pd.DataFrame.from_records(rows, columns=data_dict.keys()) .....:

In [242]: df = expand_grid( .....: {"height": [60, 70], "weight": [100, 140, 180], "sex": ["Male", "Female"]} .....: ) .....:

In [243]: df Out[243]: height weight sex 0 60 100 Male 1 60 100 Female 2 60 140 Male 3 60 140 Female 4 60 180 Male 5 60 180 Female 6 70 100 Male 7 70 100 Female 8 70 140 Male 9 70 140 Female 10 70 180 Male 11 70 180 Female

Constant series#

To assess if a series has a constant value, we can check if series.nunique() <= 1. However, a more performant approach, that does not count all unique values first, is:

In [244]: v = s.to_numpy()

In [245]: is_constant = v.shape[0] == 0 or (s[0] == s).all()

This approach assumes that the series does not contain missing values. For the case that we would drop NA values, we can simply remove those values first:

In [246]: v = s.dropna().to_numpy()

In [247]: is_constant = v.shape[0] == 0 or (s[0] == s).all()

If missing values are considered distinct from any other value, then one could use:

In [248]: v = s.to_numpy()

In [249]: is_constant = v.shape[0] == 0 or (s[0] == s).all() or not pd.notna(v).any()

(Note that this example does not disambiguate between np.nan, pd.NA and None)