Remove $ shell prompt from docs, related #418

docs/scripts/csvclean.rst

@@ -30,7 +30,7 @@ Examples
 
 Test a file with known bad rows::
 
-    $ csvclean -n examples/bad.csv
+    csvclean -n examples/bad.csv
 
     Line 3: Expected 3 columns, found 4 columns
     Line 4: Expected 3 columns, found 2 columns

docs/scripts/csvcut.rst

@@ -42,7 +42,7 @@ Examples
 
 Print the indices and names of all columns::
 
-    $ csvcut -n examples/realdata/FY09_EDU_Recipients_by_State.csv 
+    csvcut -n examples/realdata/FY09_EDU_Recipients_by_State.csv 
       1: State Name
       2: State Abbreviate
       3: Code
@@ -56,13 +56,13 @@ Print the indices and names of all columns::
 
 Extract the first and third columns::
 
-    $ csvcut -c 1,3 examples/realdata/FY09_EDU_Recipients_by_State.csv
+    csvcut -c 1,3 examples/realdata/FY09_EDU_Recipients_by_State.csv
 
 Extract columns named "TOTAL" and "State Name" (in that order)::
 
-    $ csvcut -c TOTAL,"State Name" examples/realdata/FY09_EDU_Recipients_by_State.csv
+    csvcut -c TOTAL,"State Name" examples/realdata/FY09_EDU_Recipients_by_State.csv
 
 Add line numbers to a file, making no other changes::
 
-    $ csvcut -l examples/realdata/FY09_EDU_Recipients_by_State.csv
+    csvcut -l examples/realdata/FY09_EDU_Recipients_by_State.csv
 

docs/scripts/csvformat.rst

@@ -51,9 +51,9 @@ Examples
 
 Convert  "standard" CSV file to a pipe-delimited one::
 
-    $ csvformat -D "|" examples/dummy.csv
+    csvformat -D "|" examples/dummy.csv
 
 Convert to ridiculous line-endings::
 
-    $ csvformat -M "\r" examples/dummy.csv
+    csvformat -M "\r" examples/dummy.csv
 

docs/scripts/csvgrep.rst

@@ -48,9 +48,9 @@ Examples
 
 Search for the row relating to Illinois::
 
-    $ csvgrep -c 1 -m ILLINOIS examples/realdata/FY09_EDU_Recipients_by_State.csv 
+    csvgrep -c 1 -m ILLINOIS examples/realdata/FY09_EDU_Recipients_by_State.csv 
 
 Search for rows relating to states with names beginning with the letter "I"::
 
-    $ csvgrep -c 1 -r "^I" examples/realdata/FY09_EDU_Recipients_by_State.csv 
+    csvgrep -c 1 -r "^I" examples/realdata/FY09_EDU_Recipients_by_State.csv 
 

docs/scripts/csvjson.rst

@@ -48,7 +48,7 @@ Examples
 
 Convert veteran's education dataset to JSON keyed by state abbreviation::
 
-    $ csvjson -k "State Abbreviate" -i 4 examples/realdata/FY09_EDU_Recipients_by_State.csv
+    csvjson -k "State Abbreviate" -i 4 examples/realdata/FY09_EDU_Recipients_by_State.csv
 
 Results in a JSON document like::
 
@@ -72,7 +72,7 @@ Results in a JSON document like::
 
 Converting locations of public art into GeoJSON::
 
-    $ csvjson --lat latitude --lon longitude --k slug --crs EPSG:4269 -i 4 examples/test_geo.csv
+    csvjson --lat latitude --lon longitude --k slug --crs EPSG:4269 -i 4 examples/test_geo.csv
 
 Results in a GeoJSON document like::
 

docs/scripts/csvlook.rst

@@ -32,8 +32,8 @@ Examples
 
 Basic use::
 
-    $ csvlook examples/testfixed_converted.csv
+    csvlook examples/testfixed_converted.csv
 
 This utility is especially useful as a final operation when piping through other utilities::
 
-    $ csvcut -c 9,1 examples/realdata/FY09_EDU_Recipients_by_State.csv | csvlook
+    csvcut -c 9,1 examples/realdata/FY09_EDU_Recipients_by_State.csv | csvlook

docs/scripts/csvpy.rst

@@ -35,14 +35,14 @@ Examples
 
 Basic use::
 
-    $ csvpy examples/dummy.csv
+    csvpy examples/dummy.csv
     Welcome! "examples/dummy.csv" has been loaded in a reader object named "reader".
     >>> reader.next()
     [u'a', u'b', u'c']
 
 As a dictionary::
 
-    $ csvpy --dict examples/dummy.csv -v
+    csvpy --dict examples/dummy.csv -v
     Welcome! "examples/dummy.csv" has been loaded in a DictReader object named "reader".
     >>> reader.next()
     {u'a': u'1', u'c': u'3', u'b': u'2'}

docs/scripts/csvsort.rst

@@ -39,8 +39,8 @@ Examples
 
 Sort the veteran's education benefits table by the "TOTAL" column::
 
-    $ cat examples/realdata/FY09_EDU_Recipients_by_State.csv | csvsort -c 9
+    cat examples/realdata/FY09_EDU_Recipients_by_State.csv | csvsort -c 9
 
 View the five states with the most individuals claiming veteran's education benefits::
 
-    $ cat examples/realdata/FY09_EDU_Recipients_by_State.csv | csvcut -c 1,9 | csvsort -r -c 2 | head -n 5
+    cat examples/realdata/FY09_EDU_Recipients_by_State.csv | csvcut -c 1,9 | csvsort -r -c 2 | head -n 5

docs/scripts/csvsql.rst

@@ -66,22 +66,22 @@ Examples
 
 Generate a statement in the PostgreSQL dialect::
 
-    $ csvsql -i postgresql examples/realdata/FY09_EDU_Recipients_by_State.csv
+    csvsql -i postgresql examples/realdata/FY09_EDU_Recipients_by_State.csv
 
 Create a table and import data from the CSV directly into Postgres::
 
-    $ createdb test
-    $ csvsql --db postgresql:///test --table fy09 --insert examples/realdata/FY09_EDU_Recipients_by_State.csv
+    createdb test
+    csvsql --db postgresql:///test --table fy09 --insert examples/realdata/FY09_EDU_Recipients_by_State.csv
 
 For large tables it may not be practical to process the entire table. One solution to this is to analyze a sample of the table. In this case it can be useful to turn off length limits and null checks with the ``no-constraints`` option::
     
-    $ head -n 20 examples/realdata/FY09_EDU_Recipients_by_State.csv | csvsql --no-constraints --table fy09
+    head -n 20 examples/realdata/FY09_EDU_Recipients_by_State.csv | csvsql --no-constraints --table fy09
 
 Create tables for an entire folder of CSVs and import data from those files directly into Postgres::
 
-    $ createdb test
-    $ csvsql --db postgresql:///test --insert examples/*.csv
+    createdb test
+    csvsql --db postgresql:///test --insert examples/*.csv
 
 You can also use CSVSQL to "directly" query one or more CSV files. Please note that this will create an in-memory SQL database, so it won't be very fast::
 
-    $ csvsql --query  "select m.usda_id, avg(i.sepal_length) as mean_sepal_length from iris as i join irismeta as m on (i.species = m.species) group by m.species" examples/iris.csv examples/irismeta.csv
+    csvsql --query  "select m.usda_id, avg(i.sepal_length) as mean_sepal_length from iris as i join irismeta as m on (i.species = m.species) group by m.species" examples/iris.csv examples/irismeta.csv

docs/scripts/csvstack.rst

@@ -39,4 +39,4 @@ Examples
 
 Contrived example: joining a set of homogoenous files for different years::
 
-    $ csvstack -g 2009,2010 examples/realdata/FY09_EDU_Recipients_by_State.csv examples/realdata/Datagov_FY10_EDU_recp_by_State.csv
+    csvstack -g 2009,2010 examples/realdata/FY09_EDU_Recipients_by_State.csv examples/realdata/Datagov_FY10_EDU_recp_by_State.csv

docs/scripts/csvstat.rst

@@ -47,11 +47,11 @@ Examples
 
 Basic use::
 
-    $ csvstat examples/realdata/FY09_EDU_Recipients_by_State.csv 
+    csvstat examples/realdata/FY09_EDU_Recipients_by_State.csv 
 
 When an statistic name is passed, only that stat will be printed::
 
-    $ csvstat --freq examples/realdata/FY09_EDU_Recipients_by_State.csv
+    csvstat --freq examples/realdata/FY09_EDU_Recipients_by_State.csv
 
       1. State Name: None
       2. State Abbreviate: None
@@ -66,7 +66,7 @@ When an statistic name is passed, only that stat will be printed::
  
 If a single stat *and* a single column are requested, only a value will be returned::
 
-    $ csvstat -c 4 --freq examples/realdata/FY09_EDU_Recipients_by_State.csv
+    csvstat -c 4 --freq examples/realdata/FY09_EDU_Recipients_by_State.csv
 
     3548.0
 

docs/scripts/in2csv.rst

@@ -60,7 +60,7 @@ Examples
 
 Convert the 2000 census geo headers file from fixed-width to CSV and from latin-1 encoding to utf8::
 
-    $ in2csv -e iso-8859-1 -f fixed -s examples/realdata/census_2000/census2000_geo_schema.csv examples/realdata/census_2000/usgeo_excerpt.upl > usgeo.csv
+    in2csv -e iso-8859-1 -f fixed -s examples/realdata/census_2000/census2000_geo_schema.csv examples/realdata/census_2000/usgeo_excerpt.upl > usgeo.csv
 
 .. note::
 
@@ -70,20 +70,20 @@ Convert the 2000 census geo headers file from fixed-width to CSV and from latin-
 
 Convert an Excel .xls file::
 
-    $ in2csv examples/test.xls
+    in2csv examples/test.xls
 
 Standardize the formatting of a CSV file (quoting, line endings, etc.)::
 
-    $ in2csv examples/realdata/FY09_EDU_Recipients_by_State.csv
+    in2csv examples/realdata/FY09_EDU_Recipients_by_State.csv
 
 Fetch csvkit's open issues from the Github API, convert the JSON response into a CSV and write it to a file::
 
-    $ curl https://api.github.com/repos/onyxfish/csvkit/issues?state=open | in2csv -f json -v > issues.csv 
+    curl https://api.github.com/repos/onyxfish/csvkit/issues?state=open | in2csv -f json -v > issues.csv 
 Convert a DBase DBF file to an equivalent CSV::
 
-    $ in2csv examples/testdbf.dbf > testdbf_converted.csv
+    in2csv examples/testdbf.dbf > testdbf_converted.csv
 
 Fetch the ten most recent robberies in Oakland, convert the GeoJSON response into a CSV and write it to a file::
 
-    $ curl "http://oakland.crimespotting.org/crime-data?format=json&type=robbery&count=10" | in2csv -f geojson > robberies.csv
+    curl "http://oakland.crimespotting.org/crime-data?format=json&type=robbery&count=10" | in2csv -f geojson > robberies.csv
 

docs/scripts/sql2csv.rst

@@ -34,16 +34,16 @@ Examples
 
 Load sample data into a table using :doc:`csvsql` and then query it using `sql2csv`::
 
-    $ csvsql --db "sqlite:///dummy.db" --table "test" --insert examples/dummy.csv
-    $ sql2csv --db "sqlite:///dummy.db" --query "select * from test"
+    csvsql --db "sqlite:///dummy.db" --table "test" --insert examples/dummy.csv
+    sql2csv --db "sqlite:///dummy.db" --query "select * from test"
 
 Load data about financial aid recipients into Postgresql. Then find the three states that received the most, while also filtering out empty rows::
 
-    $ createdb recipients
-    $ csvsql --db "postgresql:///recipients" --table "fy09" --insert examples/realdata/FY09_EDU_Recipients_by_State.csv
-    $ sql2csv --db "postgresql:///recipients" --query "select * from fy09 where \"State Name\" != '' order by fy09.\"TOTAL\" limit 3"
+    createdb recipients
+    csvsql --db "postgresql:///recipients" --table "fy09" --insert examples/realdata/FY09_EDU_Recipients_by_State.csv
+    sql2csv --db "postgresql:///recipients" --query "select * from fy09 where \"State Name\" != '' order by fy09.\"TOTAL\" limit 3"
 
 You can even use it as a simple SQL calculator (in this example an in-memory sqlite database is used as the default)::
 
-    $ sql2csv --query "select 300 * 47 % 14 * 27 + 7000"
+    sql2csv --query "select 300 * 47 % 14 * 27 + 7000"
 

docs/tricks.rst

@@ -7,8 +7,8 @@ Reading compressed CSVs
 
 csvkit has builtin support for reading ``gzip`` or ``bz2`` compressed input files. This is automatically detected based on the file extension. For example::
 
-    $ csvstat examples/dummy.csv.gz
-    $ csvstat examples/dummy.csv.bz2
+    csvstat examples/dummy.csv.gz
+    csvstat examples/dummy.csv.bz2
 
 Please note, the files are decompressed in memory, so this is a convenience, not an optimization.
 
@@ -17,11 +17,11 @@ Specifying STDIN as a file
 
 Most tools default to ``STDIN`` if no filename is specified, but tools like :doc:`scripts/csvjoin` and :doc:`scripts/csvstack` accept multiple files, so this is not possible. To work around this it is also possible to specify ``STDIN`` by using ``-`` as a filename. For example, these three commands are functionally identical::
 
-    $ csvstat examples/dummy.csv
-    $ cat examples/dummy.csv | csvstat
-    $ cat examples/dummy.csv | csvstat -
+    csvstat examples/dummy.csv
+    cat examples/dummy.csv | csvstat
+    cat examples/dummy.csv | csvstat -
 
 This specification allows you to, for instance, ``csvstack`` input on ``STDIN`` with another file::
 
-    $ cat ~/src/csvkit/examples/dummy.csv | csvstack ~/src/csvkit/examples/dummy3.csv -
+    cat ~/src/csvkit/examples/dummy.csv | csvstack ~/src/csvkit/examples/dummy3.csv -
 

docs/tutorial/2_examining_the_data.rst

@@ -11,7 +11,7 @@ In the previous section we saw how we could use ``csvlook`` and ``csvcut`` to pe
 
 Let's examine summary statistics for some selected columns from our data (remember you can use ``csvcut -n data.csv`` to see the columns in the data)::
 
-    $ csvcut -c county,acquisition_cost,ship_date data.csv | csvstat
+    csvcut -c county,acquisition_cost,ship_date data.csv | csvstat
       1. county
             <type 'unicode'>
             Nulls: False
@@ -69,7 +69,7 @@ csvgrep: find the data you need
 
 After reviewing the summary statistics you might wonder what equipment was received by a particular county. To get a simple answer to the question we can use :doc:`/scripts/csvgrep` to search for the state's name amongst the rows. Let's also use ``csvcut`` to just look at the columns we care about and ``csvlook`` to format the output::
 
-    $ csvcut -c county,item_name,total_cost data.csv | csvgrep -c county -m LANCASTER | csvlook
+    csvcut -c county,item_name,total_cost data.csv | csvgrep -c county -m LANCASTER | csvlook
     |------------+--------------------------------+-------------|
     |  county    | item_name                      | total_cost  |
     |------------+--------------------------------+-------------|
@@ -100,7 +100,7 @@ csvsort: order matters
 
 Now let's use :doc:`/scripts/csvsort` to sort the rows by the ``total_cost`` column, in reverse (descending) order::
 
-    $ csvcut -c county,item_name,total_cost data.csv | csvgrep -c county -m LANCASTER | csvsort -c total_cost -r | csvlook
+    csvcut -c county,item_name,total_cost data.csv | csvgrep -c county -m LANCASTER | csvsort -c total_cost -r | csvlook
     |------------+--------------------------------+-------------|
     |  county    | item_name                      | total_cost  |
     |------------+--------------------------------+-------------|

docs/tutorial/3_power_tools.rst

@@ -7,11 +7,11 @@ csvjoin: merging related data
 
 One of the most common operations that we need to perform on data is "joining" it to other, related data. For instance, given a dataset about equipment supplied to counties in Nebraska, one might reasonably want to merge that with a dataset containing the population of each county. :doc:`/scripts/csvjoin` allows us to take two those two datasets (equipment and population) and merge them, much like you might do with a SQL ``JOIN`` query. In order to demonstrate this, let's grab a second dataset::
 
-    $ curl -L -O https://github.com/onyxfish/csvkit/raw/master/examples/realdata/acs2012_5yr_population.csv
+    curl -L -O https://github.com/onyxfish/csvkit/raw/master/examples/realdata/acs2012_5yr_population.csv
 
 Now let's see what's in there::
 
-    $ csvstat acs2012_5yr_population.csv
+    csvstat acs2012_5yr_population.csv
       1. fips
             <type 'int'>
             Nulls: False
@@ -58,11 +58,11 @@ Now let's see what's in there::
 
 As you can see, this data file contains population estimates for each county in Nebraska from the 2012 5-year ACS estimates. This data was retrieved from `Census Reporter <http://censusreporter.org/>`_ and reformatted slightly for this example. Let's join it to our equipment data::
 
-    $ csvjoin -c fips data.csv acs2012_5yr_population.csv > joined.csv
+    csvjoin -c fips data.csv acs2012_5yr_population.csv > joined.csv
 
 Since both files contain a fips column, we can use that to join the two. In our output you should see the population data appended at the end of each row of data. Let's combine this with what we've learned before to answer the question "What was the lowest population county to receive equipment?"::
 
-    $ csvcut -c county,item_name,total_population joined.csv | csvsort -c total_population | csvlook | head
+    csvcut -c county,item_name,total_population joined.csv | csvsort -c total_population | csvlook | head
     |-------------+----------------------------------------------------------------+-------------------|
     |  county     | item_name                                                      | total_population  |
     |-------------+----------------------------------------------------------------+-------------------|
@@ -81,19 +81,19 @@ csvstack: combining subsets
 
 Frequently large datasets are distributed in many small files. At some point you will probably want to merge those files for aggregate analysis. :doc:`/scripts/csvstack` allows you to "stack" the rows from CSV files with identical headers. To demonstrate, let's imagine we've decided that Nebraska and Kansas form a "region" and that it would be useful to analyze them in a single dataset. Let's grab the Kansas data::
 
-    $ curl -L -O https://github.com/onyxfish/csvkit/raw/master/examples/realdata/ks_1033_data.csv
+    curl -L -O https://github.com/onyxfish/csvkit/raw/master/examples/realdata/ks_1033_data.csv
 
 Back in :doc:`1_getting_started`, we had used in2csv to convert our Nebraska data from XLSX to CSV. However, we named our output `data.csv` for simplicity at the time. Now that we are going to be stacking multiple states, we should re-convert our Nebraska data using a file naming convention matching our Kansas data::
 
-    $ in2csv ne_1033_data.xlsx > ne_1033_data.csv
+    in2csv ne_1033_data.xlsx > ne_1033_data.csv
 
 Now let's stack these two data files::
 
-    $ csvstack ne_1033_data.csv ks_1033_data.csv > region.csv
+    csvstack ne_1033_data.csv ks_1033_data.csv > region.csv
 
 Using csvstat we cansee that our ``region.csv`` contains both datasets::
 
-    $ csvstat -c state,acquisition_cost region.csv
+    csvstat -c state,acquisition_cost region.csv
       1. state
             <type 'unicode'>
             Nulls: False
@@ -126,7 +126,7 @@ Sometimes (almost always), the command line isn't enough. It would be crazy to t
 
 By default, ``csvsql`` will generate a create table statement for your data. You can specify what sort of database you are using with the ``-i`` flag::
 
-    $ csvsql -i sqlite joined.csv
+    csvsql -i sqlite joined.csv
     CREATE TABLE joined (
             state VARCHAR(2) NOT NULL,
             county VARCHAR(10) NOT NULL,
@@ -151,19 +151,19 @@ Here we have the sqlite "create table" statement for our joined data. You'll see
 
 Often you won't care about storing the SQL statements locally. You can also use ``csvsql`` to create the table directly in the database on your local machine. If you add the ``--insert`` option the data will also be imported::
 
-    $ csvsql --db sqlite:///leso.db --insert joined.csv
+    csvsql --db sqlite:///leso.db --insert joined.csv
 
 How can we check that our data was imported successfully? We could use the sqlite command line interface, but rather than worry about the specifics of another tool, we can also use ``sql2csv``::
 
-    $ sql2csv --db sqlite:///leso.db --query "select * from joined"
+    sql2csv --db sqlite:///leso.db --query "select * from joined"
 
 Note that the ``--query`` parameter to ``sql2csv`` accepts any SQL query. For example, to export Douglas county from the ``joined`` table from our sqlite database, we would run::
 
-    $ sql2csv --db sqlite:///leso.db --query "select * from joined where county='DOUGLAS';" > douglas.csv
+    sql2csv --db sqlite:///leso.db --query "select * from joined where county='DOUGLAS';" > douglas.csv
 
 Sometimes, if you will only be running a single query, even constructing the database is a waste of time. For that case, you can actually skip the database entirely and ``csvsql`` will create one in memory for you::
 
-    $ csvsql --query "select county,item_name from joined where quantity > 5;" joined.csv | csvlook
+    csvsql --query "select county,item_name from joined where quantity > 5;" joined.csv | csvlook
 
 SQL queries directly on CSVs! Keep in mind when using this that you are loading the entire dataset into an in-memory database, so it is likely to be very slow for large datasets.