values: df <- data.frame(a=rnorm(10), b=rnorm(10)) subset(df, a <= b) df[df$a <= df$b,] # note the comma 170 Chapter 2. Getting started pandas: powerful Python data analysis toolkit, Release 1.0.0 In Male No Sun Dinner 2 4 24.59 3.61 Female No Sun Dinner 4 SELECT In SQL, selection is done using a comma-separated list of columns you’d like to select (or a * to select all columns): SELECT total_bill of a character string with the LENGTHN and LENGTHC functions. LENGTHN excludes trailing blanks and LENGTHC includes trailing blanks. data _null_; set tips; put(LENGTHN(time)); (continues on next page)

list-like column in docs for more information (GH16538, GH10511) Here is a typical usecase. You have comma separated string in a column. In [12]: df = pd.DataFrame([{'var1': 'a,b,c', 'var2': 1}, ....: {'var1': values: df <- data.frame(a=rnorm(10), b=rnorm(10)) subset(df, a <= b) df[df$a <= df$b,] # note the comma In pandas, there are a few ways to perform subsetting. You can use query() or pass an expression Male No Sun Dinner 2 4 24.59 3.61 Female No Sun Dinner 4 SELECT In SQL, selection is done using a comma-separated list of columns you’d like to select (or a * to select all columns): 3.5. Comparison with

list-like column in docs for more information (GH16538, GH10511) Here is a typical usecase. You have comma separated string in a column. In [12]: df = pd.DataFrame([{'var1': 'a,b,c', 'var2': 1}, ....: {'var1': values: df <- data.frame(a=rnorm(10), b=rnorm(10)) subset(df, a <= b) df[df$a <= df$b,] # note the comma In pandas, there are a few ways to perform subsetting. You can use query() or pass an expression Male No Sun Dinner 2 4 24.59 3.61 Female No Sun Dinner 4 SELECT In SQL, selection is done using a comma-separated list of columns you’d like to select (or a * to select all columns): SELECT total_bill

pandas: powerful Python data analysis toolkit, Release 1.3.2 part before the comma is the rows you want, and the part after the comma is the columns you want to select. When using the column names, row labels use the loc operator in front of the selection brackets []. For both the part before and after the comma, you can use a single label, a list of labels, a slice of labels, a conditional expression or a colon new column Surname that contains the surname of the passengers by extracting the part before the comma. In [5]: titanic["Name"].str.split(",") Out[5]: 0 [Braund, Mr. Owen Harris] 1 [Cumings, Mrs. John

front of the selection brackets []. When using loc/iloc, the part before the comma is the rows you want, and the part after the comma is the columns you want to select. When using the column names, row labels use the loc operator in front of the selection brackets []. For both the part before and after the comma, you can use a single label, a list of labels, a slice of labels, a conditional expression or a colon new column Surname that contains the surname of the passengers by extracting the part before the comma. In [5]: titanic["Name"].str.split(",") Out[5]: 0 [Braund, Mr. Owen Harris] 1 [Cumings, Mrs. John

front of the selection brackets []. When using loc/iloc, the part before the comma is the rows you want, and the part after the comma is the columns you want to select. When using the column names, row labels use the loc operator in front of the selection brackets []. For both the part before and after the comma, you can use a single label, a list of labels, a slice of labels, a conditional expression or a colon new column Surname that contains the surname of the passengers by extracting the part before the comma. In [5]: titanic["Name"].str.split(",") Out[5]: 0 [Braund, Mr. Owen Harris] 1 [Cumings, Mrs. John

front of the selection brackets []. When using loc/iloc, the part before the comma is the rows you want, and the part after the comma is the columns you want to select. When using the column names, row labels use the loc operator in front of the selection brackets []. For both the part before and after the comma, you can use a single label, a list of labels, a slice of labels, a conditional expression or a colon new column Surname that contains the surname of the Passengers by extracting the part before the comma. 78 Chapter 1. Getting started pandas: powerful Python data analysis toolkit, Release 1.0.5 In

front of the selection brackets []. When using loc/iloc, the part before the comma is the rows you want, and the part after the comma is the columns you want to select. When using the column names, row labels use the loc operator in front of the selection brackets []. For both the part before and after the comma, you can use a single label, a list of labels, a slice of labels, a conditional expression or a colon new column Surname that contains the surname of the Passengers by extracting the part before the comma. 78 Chapter 1. Getting started pandas: powerful Python data analysis toolkit, Release 1.0.4 In

front of the selection brackets []. When using loc/iloc, the part before the comma is the rows you want, and the part after the comma is the columns you want to select. When using the column names, row labels use the loc operator in front of the selection brackets []. For both the part before and after the comma, you can use a single label, a list of labels, a slice of labels, a conditional expression or a colon new column Surname that contains the surname of the Passengers by extracting the part before the comma. 80 Chapter 2. Getting started pandas: powerful Python data analysis toolkit, Release 1.0.3 In

front of the selection brackets []. When using loc/iloc, the part before the comma is the rows you want, and the part after the comma is the columns you want to select. When using the column names, row labels use the loc operator in front of the selection brackets []. For both the part before and after the comma, you can use a single label, a list of labels, a slice of labels, a conditional expression or a colon new column Surname that contains the surname of the passengers by extracting the part before the comma. In [5]: titanic["Name"].str.split(",") Out[5]: 0 [Braund, Mr. Owen Harris] 1 [Cumings, Mrs. John