Comparison with Stata#
For potential users coming from Stata this page is meant to demonstrate how different Stata operations would be performed in pandas.
If you’re new to pandas, you might want to first read through 10 Minutes to pandas to familiarize yourself with the library.
As is customary, we import pandas and NumPy as follows:
In [1]: importpandasaspd In [2]: importnumpyasnp
Data structures#
General terminology translation#
pandas |
Stata |
|---|---|
|
data set |
column |
variable |
row |
observation |
groupby |
bysort |
|
|
DataFrame#
A DataFrame in pandas is analogous to a Stata data set – a two-dimensional
data source with labeled columns that can be of different types. As will be
shown in this document, almost any operation that can be applied to a data set
in Stata can also be accomplished in pandas.
Series#
A Series is the data structure that represents one column of a
DataFrame. Stata doesn’t have a separate data structure for a single column,
but in general, working with a Series is analogous to referencing a column
of a data set in Stata.
Index#
Every DataFrame and Series has an Index – labels on the
rows of the data. Stata does not have an exactly analogous concept. In Stata, a data set’s
rows are essentially unlabeled, other than an implicit integer index that can be
accessed with _n.
In pandas, if no index is specified, an integer index is also used by default
(first row = 0, second row = 1, and so on). While using a labeled Index or
MultiIndex can enable sophisticated analyses and is ultimately an important
part of pandas to understand, for this comparison we will essentially ignore the
Index and just treat the DataFrame as a collection of columns. Please
see the indexing documentation for much more on how to use an
Index effectively.
Copies vs. in place operations#
Most pandas operations return copies of the Series/DataFrame. To make the changes "stick",
you’ll need to either assign to a new variable:
sorted_df = df.sort_values("col1")
or overwrite the original one:
df = df.sort_values("col1")
Note
You will see an inplace=True or copy=False keyword argument available for
some methods:
df.replace(5, inplace=True)
There is an active discussion about deprecating and removing inplace and copy for
most methods (e.g. dropna) except for a very small subset of methods
(including replace). Both keywords won’t be
necessary anymore in the context of Copy-on-Write. The proposal can be found
here.
Data input / output#
Constructing a DataFrame from values#
A Stata data set can be built from specified values by
placing the data after an input statement and
specifying the column names.
input x y 1 2 3 4 5 6 end
A pandas DataFrame can be constructed in many different ways,
but for a small number of values, it is often convenient to specify it as
a Python dictionary, where the keys are the column names
and the values are the data.
In [3]: df = pd.DataFrame({"x": [1, 3, 5], "y": [2, 4, 6]}) In [4]: df Out[4]: x y 0 1 2 1 3 4 2 5 6
Reading external data#
Like Stata, pandas provides utilities for reading in data from
many formats. The tips data set, found within the pandas
tests (csv)
will be used in many of the following examples.
Stata provides import delimited to read csv data into a data set in memory.
If the tips.csv file is in the current working directory, we can import it as follows.
import delimited tips.csv
The pandas method is read_csv(), which works similarly. Additionally, it will automatically download
the data set if presented with a url.
In [5]: url = ( ...: "https://raw.githubusercontent.com/pandas-dev" ...: "/pandas/main/pandas/tests/io/data/csv/tips.csv" ...: ) ...: In [6]: tips = pd.read_csv(url) In [7]: tips Out[7]: total_bill tip sex smoker day time size 0 16.99 1.01 Female No Sun Dinner 2 1 10.34 1.66 Male No Sun Dinner 3 2 21.01 3.50 Male No Sun Dinner 3 3 23.68 3.31 Male No Sun Dinner 2 4 24.59 3.61 Female No Sun Dinner 4 .. ... ... ... ... ... ... ... 239 29.03 5.92 Male No Sat Dinner 3 240 27.18 2.00 Female Yes Sat Dinner 2 241 22.67 2.00 Male Yes Sat Dinner 2 242 17.82 1.75 Male No Sat Dinner 2 243 18.78 3.00 Female No Thur Dinner 2 [244 rows x 7 columns]
Like import delimited, read_csv() can take a number of parameters to specify
how the data should be parsed. For example, if the data were instead tab delimited,
did not have column names, and existed in the current working directory,
the pandas command would be:
tips = pd.read_csv("tips.csv", sep="\t", header=None) # alternatively, read_table is an alias to read_csv with tab delimiter tips = pd.read_table("tips.csv", header=None)
pandas can also read Stata data sets in .dta format with the read_stata() function.
df = pd.read_stata("data.dta")
In addition to text/csv and Stata files, pandas supports a variety of other data formats
such as Excel, SAS, HDF5, Parquet, and SQL databases. These are all read via a pd.read_*
function. See the IO documentation for more details.
Limiting output#
By default, pandas will truncate output of large DataFrames to show the first and last rows.
This can be overridden by changing the pandas options, or using
DataFrame.head() or DataFrame.tail().
In [8]: tips.head(5) Out[8]: total_bill tip sex smoker day time size 0 16.99 1.01 Female No Sun Dinner 2 1 10.34 1.66 Male No Sun Dinner 3 2 21.01 3.50 Male No Sun Dinner 3 3 23.68 3.31 Male No Sun Dinner 2 4 24.59 3.61 Female No Sun Dinner 4
The equivalent in Stata would be:
list in 1/5
Exporting data#
The inverse of import delimited in Stata is export delimited
export delimited tips2.csv
Similarly in pandas, the opposite of read_csv is DataFrame.to_csv().
tips.to_csv("tips2.csv")
pandas can also export to Stata file format with the DataFrame.to_stata() method.
tips.to_stata("tips2.dta")
Data operations#
Operations on columns#
In Stata, arbitrary math expressions can be used with the generate and
replace commands on new or existing columns. The drop command drops
the column from the data set.
replace total_bill = total_bill - 2 generate new_bill = total_bill / 2 drop new_bill
pandas provides vectorized operations by specifying the individual Series in the
DataFrame. New columns can be assigned in the same way. The DataFrame.drop() method drops
a column from the DataFrame.
In [9]: tips["total_bill"] = tips["total_bill"] - 2 In [10]: tips["new_bill"] = tips["total_bill"] / 2 In [11]: tips Out[11]: total_bill tip sex smoker day time size new_bill 0 14.99 1.01 Female No Sun Dinner 2 7.495 1 8.34 1.66 Male No Sun Dinner 3 4.170 2 19.01 3.50 Male No Sun Dinner 3 9.505 3 21.68 3.31 Male No Sun Dinner 2 10.840 4 22.59 3.61 Female No Sun Dinner 4 11.295 .. ... ... ... ... ... ... ... ... 239 27.03 5.92 Male No Sat Dinner 3 13.515 240 25.18 2.00 Female Yes Sat Dinner 2 12.590 241 20.67 2.00 Male Yes Sat Dinner 2 10.335 242 15.82 1.75 Male No Sat Dinner 2 7.910 243 16.78 3.00 Female No Thur Dinner 2 8.390 [244 rows x 8 columns] In [12]: tips = tips.drop("new_bill", axis=1)
Filtering#
Filtering in Stata is done with an if clause on one or more columns.
list if total_bill > 10
DataFrames can be filtered in multiple ways; the most intuitive of which is using boolean indexing.
In [13]: tips[tips["total_bill"] > 10] Out[13]: total_bill tip sex smoker day time size 0 14.99 1.01 Female No Sun Dinner 2 2 19.01 3.50 Male No Sun Dinner 3 3 21.68 3.31 Male No Sun Dinner 2 4 22.59 3.61 Female No Sun Dinner 4 5 23.29 4.71 Male No Sun Dinner 4 .. ... ... ... ... ... ... ... 239 27.03 5.92 Male No Sat Dinner 3 240 25.18 2.00 Female Yes Sat Dinner 2 241 20.67 2.00 Male Yes Sat Dinner 2 242 15.82 1.75 Male No Sat Dinner 2 243 16.78 3.00 Female No Thur Dinner 2 [204 rows x 7 columns]
The above statement is simply passing a Series of True/False objects to the DataFrame,
returning all rows with True.
In [14]: is_dinner = tips["time"] == "Dinner" In [15]: is_dinner Out[15]: 0 True 1 True 2 True 3 True 4 True ... 239 True 240 True 241 True 242 True 243 True Name: time, Length: 244, dtype: bool In [16]: is_dinner.value_counts() Out[16]: time True 176 False 68 Name: count, dtype: int64 In [17]: tips[is_dinner] Out[17]: total_bill tip sex smoker day time size 0 14.99 1.01 Female No Sun Dinner 2 1 8.34 1.66 Male No Sun Dinner 3 2 19.01 3.50 Male No Sun Dinner 3 3 21.68 3.31 Male No Sun Dinner 2 4 22.59 3.61 Female No Sun Dinner 4 .. ... ... ... ... ... ... ... 239 27.03 5.92 Male No Sat Dinner 3 240 25.18 2.00 Female Yes Sat Dinner 2 241 20.67 2.00 Male Yes Sat Dinner 2 242 15.82 1.75 Male No Sat Dinner 2 243 16.78 3.00 Female No Thur Dinner 2 [176 rows x 7 columns]
If/then logic#
In Stata, an if clause can also be used to create new columns.
generate bucket = "low" if total_bill < 10 replace bucket = "high" if total_bill >= 10
The same operation in pandas can be accomplished using
the where method from numpy.
In [18]: tips["bucket"] = np.where(tips["total_bill"] < 10, "low", "high") In [19]: tips Out[19]: total_bill tip sex smoker day time size bucket 0 14.99 1.01 Female No Sun Dinner 2 high 1 8.34 1.66 Male No Sun Dinner 3 low 2 19.01 3.50 Male No Sun Dinner 3 high 3 21.68 3.31 Male No Sun Dinner 2 high 4 22.59 3.61 Female No Sun Dinner 4 high .. ... ... ... ... ... ... ... ... 239 27.03 5.92 Male No Sat Dinner 3 high 240 25.18 2.00 Female Yes Sat Dinner 2 high 241 20.67 2.00 Male Yes Sat Dinner 2 high 242 15.82 1.75 Male No Sat Dinner 2 high 243 16.78 3.00 Female No Thur Dinner 2 high [244 rows x 8 columns]
Date functionality#
Stata provides a variety of functions to do operations on date/datetime columns.
generate date1 = mdy(1, 15, 2013) generate date2 = date("Feb152015", "MDY") generate date1_year = year(date1) generate date2_month = month(date2) * shift date to beginning of next month generate date1_next = mdy(month(date1) + 1, 1, year(date1)) if month(date1) != 12 replace date1_next = mdy(1, 1, year(date1) + 1) if month(date1) == 12 generate months_between = mofd(date2) - mofd(date1) list date1 date2 date1_year date2_month date1_next months_between
The equivalent pandas operations are shown below. In addition to these functions, pandas supports other Time Series features not available in Stata (such as time zone handling and custom offsets) – see the timeseries documentation for more details.
In [20]: tips["date1"] = pd.Timestamp("2013年01月15日") In [21]: tips["date2"] = pd.Timestamp("2015年02月15日") In [22]: tips["date1_year"] = tips["date1"].dt.year In [23]: tips["date2_month"] = tips["date2"].dt.month In [24]: tips["date1_next"] = tips["date1"] + pd.offsets.MonthBegin() In [25]: tips["months_between"] = tips["date2"].dt.to_period("M") - tips[ ....: "date1" ....: ].dt.to_period("M") ....: In [26]: tips[ ....: ["date1", "date2", "date1_year", "date2_month", "date1_next", "months_between"] ....: ] ....: Out[26]: date1 date2 date1_year date2_month date1_next months_between 0 2013年01月15日 2015年02月15日 2013 2 2013年02月01日 <25 * MonthEnds> 1 2013年01月15日 2015年02月15日 2013 2 2013年02月01日 <25 * MonthEnds> 2 2013年01月15日 2015年02月15日 2013 2 2013年02月01日 <25 * MonthEnds> 3 2013年01月15日 2015年02月15日 2013 2 2013年02月01日 <25 * MonthEnds> 4 2013年01月15日 2015年02月15日 2013 2 2013年02月01日 <25 * MonthEnds> .. ... ... ... ... ... ... 239 2013年01月15日 2015年02月15日 2013 2 2013年02月01日 <25 * MonthEnds> 240 2013年01月15日 2015年02月15日 2013 2 2013年02月01日 <25 * MonthEnds> 241 2013年01月15日 2015年02月15日 2013 2 2013年02月01日 <25 * MonthEnds> 242 2013年01月15日 2015年02月15日 2013 2 2013年02月01日 <25 * MonthEnds> 243 2013年01月15日 2015年02月15日 2013 2 2013年02月01日 <25 * MonthEnds> [244 rows x 6 columns]
Selection of columns#
Stata provides keywords to select, drop, and rename columns.
keep sex total_bill tip drop sex rename total_bill total_bill_2
The same operations are expressed in pandas below.
Keep certain columns#
In [27]: tips[["sex", "total_bill", "tip"]] Out[27]: sex total_bill tip 0 Female 14.99 1.01 1 Male 8.34 1.66 2 Male 19.01 3.50 3 Male 21.68 3.31 4 Female 22.59 3.61 .. ... ... ... 239 Male 27.03 5.92 240 Female 25.18 2.00 241 Male 20.67 2.00 242 Male 15.82 1.75 243 Female 16.78 3.00 [244 rows x 3 columns]
Drop a column#
In [28]: tips.drop("sex", axis=1) Out[28]: total_bill tip smoker day time size 0 14.99 1.01 No Sun Dinner 2 1 8.34 1.66 No Sun Dinner 3 2 19.01 3.50 No Sun Dinner 3 3 21.68 3.31 No Sun Dinner 2 4 22.59 3.61 No Sun Dinner 4 .. ... ... ... ... ... ... 239 27.03 5.92 No Sat Dinner 3 240 25.18 2.00 Yes Sat Dinner 2 241 20.67 2.00 Yes Sat Dinner 2 242 15.82 1.75 No Sat Dinner 2 243 16.78 3.00 No Thur Dinner 2 [244 rows x 6 columns]
Rename a column#
In [29]: tips.rename(columns={"total_bill": "total_bill_2"}) Out[29]: total_bill_2 tip sex smoker day time size 0 14.99 1.01 Female No Sun Dinner 2 1 8.34 1.66 Male No Sun Dinner 3 2 19.01 3.50 Male No Sun Dinner 3 3 21.68 3.31 Male No Sun Dinner 2 4 22.59 3.61 Female No Sun Dinner 4 .. ... ... ... ... ... ... ... 239 27.03 5.92 Male No Sat Dinner 3 240 25.18 2.00 Female Yes Sat Dinner 2 241 20.67 2.00 Male Yes Sat Dinner 2 242 15.82 1.75 Male No Sat Dinner 2 243 16.78 3.00 Female No Thur Dinner 2 [244 rows x 7 columns]
Sorting by values#
Sorting in Stata is accomplished via sort
sort sex total_bill
pandas has a DataFrame.sort_values() method, which takes a list of columns to sort by.
In [30]: tips = tips.sort_values(["sex", "total_bill"]) In [31]: tips Out[31]: total_bill tip sex smoker day time size 67 1.07 1.00 Female Yes Sat Dinner 1 92 3.75 1.00 Female Yes Fri Dinner 2 111 5.25 1.00 Female No Sat Dinner 1 145 6.35 1.50 Female No Thur Lunch 2 135 6.51 1.25 Female No Thur Lunch 2 .. ... ... ... ... ... ... ... 182 43.35 3.50 Male Yes Sun Dinner 3 156 46.17 5.00 Male No Sun Dinner 6 59 46.27 6.73 Male No Sat Dinner 4 212 46.33 9.00 Male No Sat Dinner 4 170 48.81 10.00 Male Yes Sat Dinner 3 [244 rows x 7 columns]
String processing#
Finding length of string#
Stata determines the length of a character string with the strlen() and
ustrlen() functions for ASCII and Unicode strings, respectively.
generate strlen_time = strlen(time) generate ustrlen_time = ustrlen(time)
You can find the length of a character string with Series.str.len().
In Python 3, all strings are Unicode strings. len includes trailing blanks.
Use len and rstrip to exclude trailing blanks.
In [32]: tips["time"].str.len() Out[32]: 67 6 92 6 111 6 145 5 135 5 .. 182 6 156 6 59 6 212 6 170 6 Name: time, Length: 244, dtype: int64 In [33]: tips["time"].str.rstrip().str.len() Out[33]: 67 6 92 6 111 6 145 5 135 5 .. 182 6 156 6 59 6 212 6 170 6 Name: time, Length: 244, dtype: int64
Finding position of substring#
Stata determines the position of a character in a string with the strpos() function.
This takes the string defined by the first argument and searches for the
first position of the substring you supply as the second argument.
generate str_position = strpos(sex, "ale")
You can find the position of a character in a column of strings with the Series.str.find()
method. find searches for the first position of the substring. If the substring is found, the
method returns its position. If not found, it returns -1. Keep in mind that Python indexes are
zero-based.
In [34]: tips["sex"].str.find("ale") Out[34]: 67 3 92 3 111 3 145 3 135 3 .. 182 1 156 1 59 1 212 1 170 1 Name: sex, Length: 244, dtype: int64
Extracting substring by position#
Stata extracts a substring from a string based on its position with the substr() function.
generate short_sex = substr(sex, 1, 1)
With pandas you can use [] notation to extract a substring
from a string by position locations. Keep in mind that Python
indexes are zero-based.
In [35]: tips["sex"].str[0:1] Out[35]: 67 F 92 F 111 F 145 F 135 F .. 182 M 156 M 59 M 212 M 170 M Name: sex, Length: 244, dtype: object
Extracting nth word#
The Stata word() function returns the nth word from a string.
The first argument is the string you want to parse and the
second argument specifies which word you want to extract.
clear input str20 string "John Smith" "Jane Cook" end generate first_name = word(name, 1) generate last_name = word(name, -1)
The simplest way to extract words in pandas is to split the strings by spaces, then reference the word by index. Note there are more powerful approaches should you need them.
In [36]: firstlast = pd.DataFrame({"String": ["John Smith", "Jane Cook"]}) In [37]: firstlast["First_Name"] = firstlast["String"].str.split(" ", expand=True)[0] In [38]: firstlast["Last_Name"] = firstlast["String"].str.rsplit(" ", expand=True)[1] In [39]: firstlast Out[39]: String First_Name Last_Name 0 John Smith John Smith 1 Jane Cook Jane Cook
Changing case#
The Stata strupper(), strlower(), strproper(),
ustrupper(), ustrlower(), and ustrtitle() functions
change the case of ASCII and Unicode strings, respectively.
clear input str20 string "John Smith" "Jane Cook" end generate upper = strupper(string) generate lower = strlower(string) generate title = strproper(string) list
The equivalent pandas methods are Series.str.upper(), Series.str.lower(), and
Series.str.title().
In [40]: firstlast = pd.DataFrame({"string": ["John Smith", "Jane Cook"]}) In [41]: firstlast["upper"] = firstlast["string"].str.upper() In [42]: firstlast["lower"] = firstlast["string"].str.lower() In [43]: firstlast["title"] = firstlast["string"].str.title() In [44]: firstlast Out[44]: string upper lower title 0 John Smith JOHN SMITH john smith John Smith 1 Jane Cook JANE COOK jane cook Jane Cook
Merging#
The following tables will be used in the merge examples:
In [45]: df1 = pd.DataFrame({"key": ["A", "B", "C", "D"], "value": np.random.randn(4)}) In [46]: df1 Out[46]: key value 0 A 0.469112 1 B -0.282863 2 C -1.509059 3 D -1.135632 In [47]: df2 = pd.DataFrame({"key": ["B", "D", "D", "E"], "value": np.random.randn(4)}) In [48]: df2 Out[48]: key value 0 B 1.212112 1 D -0.173215 2 D 0.119209 3 E -1.044236
In Stata, to perform a merge, one data set must be in memory
and the other must be referenced as a file name on disk. In
contrast, Python must have both DataFrames already in memory.
By default, Stata performs an outer join, where all observations
from both data sets are left in memory after the merge. One can
keep only observations from the initial data set, the merged data set,
or the intersection of the two by using the values created in the
_merge variable.
* First create df2 and save to disk clear input str1 key B D D E end generate value = rnormal() save df2.dta * Now create df1 in memory clear input str1 key A B C D end generate value = rnormal() preserve * Left join merge 1:n key using df2.dta keep if _merge == 1 * Right join restore, preserve merge 1:n key using df2.dta keep if _merge == 2 * Inner join restore, preserve merge 1:n key using df2.dta keep if _merge == 3 * Outer join restore merge 1:n key using df2.dta
pandas DataFrames have a merge() method, which provides similar functionality. The
data does not have to be sorted ahead of time, and different join types are accomplished via the
how keyword.
In [49]: inner_join = df1.merge(df2, on=["key"], how="inner") In [50]: inner_join Out[50]: key value_x value_y 0 B -0.282863 1.212112 1 D -1.135632 -0.173215 2 D -1.135632 0.119209 In [51]: left_join = df1.merge(df2, on=["key"], how="left") In [52]: left_join Out[52]: key value_x value_y 0 A 0.469112 NaN 1 B -0.282863 1.212112 2 C -1.509059 NaN 3 D -1.135632 -0.173215 4 D -1.135632 0.119209 In [53]: right_join = df1.merge(df2, on=["key"], how="right") In [54]: right_join Out[54]: key value_x value_y 0 B -0.282863 1.212112 1 D -1.135632 -0.173215 2 D -1.135632 0.119209 3 E NaN -1.044236 In [55]: outer_join = df1.merge(df2, on=["key"], how="outer") In [56]: outer_join Out[56]: key value_x value_y 0 A 0.469112 NaN 1 B -0.282863 1.212112 2 C -1.509059 NaN 3 D -1.135632 -0.173215 4 D -1.135632 0.119209 5 E NaN -1.044236
Missing data#
Both pandas and Stata have a representation for missing data.
pandas represents missing data with the special float value NaN (not a number). Many of the
semantics are the same; for example missing data propagates through numeric operations, and is
ignored by default for aggregations.
In [57]: outer_join Out[57]: key value_x value_y 0 A 0.469112 NaN 1 B -0.282863 1.212112 2 C -1.509059 NaN 3 D -1.135632 -0.173215 4 D -1.135632 0.119209 5 E NaN -1.044236 In [58]: outer_join["value_x"] + outer_join["value_y"] Out[58]: 0 NaN 1 0.929249 2 NaN 3 -1.308847 4 -1.016424 5 NaN dtype: float64 In [59]: outer_join["value_x"].sum() Out[59]: -3.5940742896293765
One difference is that missing data cannot be compared to its sentinel value. For example, in Stata you could do this to filter missing values.
* Keep missing values list if value_x == . * Keep non-missing values list if value_x != .
In pandas, Series.isna() and Series.notna() can be used to filter the rows.
In [60]: outer_join[outer_join["value_x"].isna()] Out[60]: key value_x value_y 5 E NaN -1.044236 In [61]: outer_join[outer_join["value_x"].notna()] Out[61]: key value_x value_y 0 A 0.469112 NaN 1 B -0.282863 1.212112 2 C -1.509059 NaN 3 D -1.135632 -0.173215 4 D -1.135632 0.119209
pandas provides a variety of methods to work with missing data. Here are some examples:
Drop rows with missing values#
In [62]: outer_join.dropna() Out[62]: key value_x value_y 1 B -0.282863 1.212112 3 D -1.135632 -0.173215 4 D -1.135632 0.119209
Forward fill from previous rows#
In [63]: outer_join.ffill() Out[63]: key value_x value_y 0 A 0.469112 NaN 1 B -0.282863 1.212112 2 C -1.509059 1.212112 3 D -1.135632 -0.173215 4 D -1.135632 0.119209 5 E -1.135632 -1.044236
Replace missing values with a specified value#
Using the mean:
In [64]: outer_join["value_x"].fillna(outer_join["value_x"].mean()) Out[64]: 0 0.469112 1 -0.282863 2 -1.509059 3 -1.135632 4 -1.135632 5 -0.718815 Name: value_x, dtype: float64
GroupBy#
Aggregation#
Stata’s collapse can be used to group by one or
more key variables and compute aggregations on
numeric columns.
collapse (sum) total_bill tip, by(sex smoker)
pandas provides a flexible groupby mechanism that allows similar aggregations. See the
groupby documentation for more details and examples.
In [65]: tips_summed = tips.groupby(["sex", "smoker"])[["total_bill", "tip"]].sum() In [66]: tips_summed Out[66]: total_bill tip sex smoker Female No 869.68 149.77 Yes 527.27 96.74 Male No 1725.75 302.00 Yes 1217.07 183.07
Transformation#
In Stata, if the group aggregations need to be used with the
original data set, one would usually use bysort with egen().
For example, to subtract the mean for each observation by smoker group.
bysort sex smoker: egen group_bill = mean(total_bill) generate adj_total_bill = total_bill - group_bill
pandas provides a Transformation mechanism that allows these type of operations to be succinctly expressed in one operation.
In [67]: gb = tips.groupby("smoker")["total_bill"] In [68]: tips["adj_total_bill"] = tips["total_bill"] - gb.transform("mean") In [69]: tips Out[69]: total_bill tip sex smoker day time size adj_total_bill 67 1.07 1.00 Female Yes Sat Dinner 1 -17.686344 92 3.75 1.00 Female Yes Fri Dinner 2 -15.006344 111 5.25 1.00 Female No Sat Dinner 1 -11.938278 145 6.35 1.50 Female No Thur Lunch 2 -10.838278 135 6.51 1.25 Female No Thur Lunch 2 -10.678278 .. ... ... ... ... ... ... ... ... 182 43.35 3.50 Male Yes Sun Dinner 3 24.593656 156 46.17 5.00 Male No Sun Dinner 6 28.981722 59 46.27 6.73 Male No Sat Dinner 4 29.081722 212 46.33 9.00 Male No Sat Dinner 4 29.141722 170 48.81 10.00 Male Yes Sat Dinner 3 30.053656 [244 rows x 8 columns]
By group processing#
In addition to aggregation, pandas groupby can be used to
replicate most other bysort processing from Stata. For example,
the following example lists the first observation in the current
sort order by sex/smoker group.
bysort sex smoker: list if _n == 1
In pandas this would be written as:
In [70]: tips.groupby(["sex", "smoker"]).first() Out[70]: total_bill tip day time size adj_total_bill sex smoker Female No 5.25 1.00 Sat Dinner 1 -11.938278 Yes 1.07 1.00 Sat Dinner 1 -17.686344 Male No 5.51 2.00 Thur Lunch 2 -11.678278 Yes 5.25 5.15 Sun Dinner 2 -13.506344
Other considerations#
Disk vs memory#
pandas and Stata both operate exclusively in memory. This means that the size of
data able to be loaded in pandas is limited by your machine’s memory.
If out of core processing is needed, one possibility is the
dask.dataframe
library, which provides a subset of pandas functionality for an
on-disk DataFrame.