API

class dataswim.Ds(df=None, db=None, nbload_libs=True)

Bases: dataswim.db.Db, dataswim.data.Df, dataswim.charts.Plot, dataswim.maps.Map, dataswim.report.Report, dataswim.base.DsBase

Main class

add(col: str, value)

Add a column with default values

Parameters:

• col (str) – column name
• value (any) – column value

Example:

ds.add("Col 4", 0)

aenc(key, value)

Add an entry to the altair encoding dict

altair_encode = {}

altair_header_()

Returns html script tags for Altair

amap(lat, long, zoom=13, tiles='map')

Sets a map

append(vals: list, index=None)

Append a row to the main dataframe

Parameters:

• vals (list) – list of the row values to add
• index – index key, defaults to None
• index – any, optional

Example:

ds.append([0, 2, 2, 3, 4])

apply(function, *cols, axis=1, **kwargs)

Apply a function on columns values

Parameters:

• function (function) – a function to apply to the columns
• cols (name of columns) – columns names
• axis – index (0) or column (1), default is 1
• kwargs (optional) – arguments for df.apply

Example:

def f(row):
        # add a new column with a value
        row["newcol"] = row["Col 1] + 1
        return row

ds.apply(f)

area_(label=None, style=None, opts=None, options={})

Get an area chart

arrow_(xloc, yloc, text, orientation='v', arrowstyle='->')

Returns an arrow for a chart. Params: the text, xloc and yloc are coordinates to position the arrow. Orientation is the way to display the arrow: possible values are [<, ^, >, v]. Arrow style is the graphic style of the arrow: possible values: [-, ->, -[, -|>, <->, <|-|>]

autoprint = False

backup()

Backup the main dataframe

backup_df = None

bar_(label=None, style=None, opts=None, options={})

Get a bar chart

bar_num_(label=None, style=None, opts=None)

Get an Altair bar + number marks chart

bokeh_header_()

Returns html script tags for Bokeh

chart(x=None, y=None, chart_type=None, opts=None, style=None, label=None, options={}, **kwargs)

Get a chart

chart_(x=None, y=None, chart_type=None, opts=None, style=None, label=None, options={}, **kwargs)

Get a chart

chart_obj = None

chart_opts = {'width': 880}

chart_style = {}

chartjs_header_()

Returns html script tags for Chartjs

circle_(label=None, style=None, opts=None, options={})

Get a circle chart

clone_(quiet=False)

Clone the DataSwim instance

Parameters:quiet (bool, optional) – print a message, defaults to False Returns:a dataswim instance Return type:Ds

color(val)

Change the chart’s color

color_(i=None)

Get a color from the palette

color_index = 0

cols_() → pandas.core.frame.DataFrame

Returns a dataframe with columns info

Returns:a pandas dataframe Return type:pd.DataFrame Example:ds.cols_()

concat(*dss, **kwargs)

Concatenate dataswim instances from and

set it to the main dataframe

Parameters:

• dss (Ds) – dataswim instances to concatenate
• kwargs – keyword arguments for pd.concat

concat_(*dss, **kwargs)

Concatenate dataswim instances and

return a new Ds instance

Parameters:

• dss (Ds) – dataswim instances to concatenate
• kwargs – keyword arguments for pd.concat

Return type:

Ds

connect(url: str)

Connect to the database and set it as main database

Parameters:url (str) – path to the database, uses the Sqlalchemy format Example:ds.connect("sqlite:///mydb.slqite")

contains(column, value)

Set the main dataframe instance to rows that contains a string value in a column

copycol(origin_col: str, dest_col: str)

Copy a columns values in another column

Parameters:

• origin_col (str) – name of the column to copy
• dest_col (str) – name of the new column

Example:

ds.copy("col 1", "New col")

count()

Counts the number of rows of the main dataframe

count_() → int

Returns the number of rows of the main dataframe

Returns:number of rows Return type:int

count_empty(field: str)

List of empty row indices

Parameters:field (str) – column to count from

count_nulls(field: str)

Count the number of null values in a column

Parameters:field (str) – the column to count from

count_unique_(field: str) → int

Return the number of unique values in a column

Parameters:field (str) – column to count from Returns:number of unique values Return type:int

count_zero(field: str)

List of row with 0 values

Parameters:field (str) – column to count from

cvar_(col)

Returns the coefficient of variance of a column in percentage

datapath = None

date(col: str, **kwargs)

Convert a column to date type

Parameters:

• col (str) – column name
• **kwargs (optional) – keyword arguments for pd.to_datetime

Example:

ds.date("mycol")

dateindex(col: str)

Set a datetime index from a column

Parameters:col (str) – column name where to index the date from Example:ds.dateindex("mycol")

dateparser(dformat='%d/%m/%Y')

Returns a date parser for pandas

daterange(datecol: str, date_start: datetime.datetime, op: str, **args)

Set the main dataframe rows in a date range

Parameters:

• datecol (str) – the column to use for range
• date_start (datetime.datetime) – the date to start from
• op (str) –
  • or -

daterange_(datecol: str, date_start: datetime.datetime, op: str, **args) → Ds

Returns a DataSwim instance with rows in a date range

Parameters:

• datecol (str) – the column to use for range
• date_start (datetime.datetime) – the date to start from
• op (str) –
  • or -

Returns:

a dataswim instance

Return type:

Ds

db = None

debug(*msg)

Prints a warning

defaults()

Reset the chart options and style to defaults

density_(label=None, style=None, opts=None)

Get a Seaborn density chart

describe_()

Return a description of the data

Returns:a pandas dataframe Return type:pd.DataFrame Example:ds.describe()

df = None

diffm(diffcol: str, name: str = 'Diff', default=nan)

Add a diff column to the main dataframe: calculate the diff from the column mean

Parameters:

• diffcol (str) – column to diff from
• name – diff column name, defaults to “Diff”
• name – str, optional
• default – column default value, defaults to nan
• default – optional

Example:

ds.diffm("Col 1", "New col")

diffn(diffcol: str, name: str = 'Diff')

Add a diff column to the main dataframe: calculate the diff from the next value

Parameters:

• diffcol (str) – column to diff from
• name (str, optional) – diff column name, defaults to “Diff”

Example:

ds.diffn("Col 1", "New col")

diffp(diffcol: str, name: str = 'Diff')

Add a diff column to the main dataframe: calculate the diff from the previous value

Parameters:

• diffcol (str) – column to diff from
• name (str, optional) – diff column name, defaults to “Diff”

Example:

ds.diffp("Col 1", "New col")

diffs(col: str, serie: iterable, name: str = 'Diff')

Add a diff column from a serie. The serie is an iterable of the same length than the dataframe

Parameters:

• col (str) – column to diff
• serie (iterable) – serie to diff from
• name – name of the diff col, defaults to “Diff”
• name – str, optional

Example:

ds.diffs("Col 1", [1, 1, 4], "New col")

diffsp(col: str, serie: iterable, name: str = 'Diff')

Add a diff column in percentage from a serie. The serie is an iterable of the same length than the dataframe

Parameters:

• col (str) – column to diff
• serie (iterable) – serie to diff from
• name – name of the diff col, defaults to “Diff”
• name – str, optional

Example:

ds.diffp("Col 1", [1, 1, 4], "New col")

distrib_(label=None, style=None, opts=None)

Get a Seaborn distribution chart

dlinear_(label=None, style=None, opts=None)

Get a Seaborn linear + distribution chart

drop(*cols)

Drops columns from the main dataframe

Parameters:cols (str) – names of the columns Example:ds.drop("Col 1", "Col 2")

drop_nan(col: str = None, method: str = 'all', **kwargs)

Drop rows with NaN values from the main dataframe

Parameters:

• col (str, optional) – name of the column, defaults to None. Drops in
• method (str, optional) – how param for df.dropna, defaults to “all”
• **kwargs (optional) – params for df.dropna

Example:

ds.drop_nan("mycol")

dropr(*rows)

Drops some rows from the main dataframe

Parameters:rows (list of ints) – rows names Example:ds.drop_rows([0, 2])

dsmap = None

end(*msg)

Prints an end message with elapsed time

engine = 'bokeh'

err(*args)

Handle an error

errorbar_(label=None, style=None, opts=None, options={})

Get a point chart

errors_handling = 'exceptions'

exact(column, *values)

Sets the main dataframe to rows that has the exact string value in a column

exact_(column, *values)

Returns a Dataswim instance with rows that has the exact string value in a column

exclude(col: str, val)

Delete rows based on value

Parameters:

• col (str) – column name
• val (any) – value to delete

Example:

ds.exclude("Col 1", "value")

fdate(*cols, precision: str = 'S', format: str = None)

Convert column values to formated date string

Parameters:

• *cols (str, at least one) – names of the colums
• precision (str, optional) – time precision: Y, M, D, H, Min S, defaults to “S”
• format (str, optional) – python date format, defaults to None

Example:

ds.fdate("mycol1", "mycol2", precision)

fill_nan(val: str, *cols)

Fill NaN values with new values in the main dataframe

Parameters:

• val (str) – new value
• *cols (str, at least one) – names of the colums

Example:

ds.fill_nan("new value", "mycol1", "mycol2")

fill_nulls(col: str)

Fill all null values with NaN values in a column. Null values are None or en empty string

Parameters:col (str) – column name Example:ds.fill_nulls("mycol")

first_() → pandas.core.series.Series

Select the first row

Returns:the first row as a serie Return type:pd.Series

flat_(col, nums=True)

Returns a flat representation of a column’s values

footer = None

format_date_(date: datetime.datetime) → str

Format a date string

Parameters:date (datetime.datetime) – the input date Returns:output date string Return type:str

get_html(chart_obj=None, slug=None)

Get the html and script tag for a chart

getall_(table)

Get all rows values for a table

gmean_(col: str, index_col: bool = True) → Ds

Group by and mean column

Parameters:

• col (str) – column to group
• index_col (bool) –

Returns:

a dataswim instance

Return type:

Ds

Example:

ds2 = ds.gmean("Col 1")

gsum_(col: str, index_col: bool = True) → Ds

Group by and sum column

Parameters:

• col (str) – column to group
• index_col (bool) –

Returns:

a dataswim instance

Return type:

Ds

Example:

ds2 = ds.gsum("Col 1")

header = None

heatmap_(label=None, style=None, opts=None, options={})

Get a heatmap chart

height(val)

Change the chart’s height

hist_(label=None, style=None, opts=None, options={})

Get an historiogram chart

hline_(label=None, style=None, opts=None, options={})

Get a mean line chart

html(label, *msg)

Prints html in notebook

index(col: str)

Set an index to the main dataframe

Parameters:col (str) – column name where to index from Example:ds.index("mycol")

indexcol(col: str)

Add a column from the index

Parameters:col (str) – name of the new column Example:ds.index_col("New col")

influx_cli = None

influx_count_(measurement)

Count the number of rows for a measurement

influx_init(url, port, user, pwd, db)

Initialize an Influxdb database client

influx_query_(q)

Runs an Influx db query

influx_to_csv(measurement, batch_size=5000)

Batch export data from an Influxdb measurement to csv

info(*msg)

Prints a message with an info prefix

insert(table: str, records: dict, create_cols: bool = False, dtypes: List[sqlalchemy.sql.sqltypes.SchemaType] = None)

Insert one or many records in the database from a dictionary

or a list of dictionaries

Parameters:

• table (str) – the table to insert into
• records (dict) – a dictionnary or list of dictionnaries of the data to insert
• create_cols (bool, optional) – create the columns if they don’t exist, defaults to False
• dtypes (SchemaType, optional) – list of SqlAlchemy table types, defaults to None. The types are infered if not provided

keep(*cols)

Limit the dataframe to some columns

Parameters:cols (str) – names of the columns Example:ds.keep("Col 1", "Col 2")

keep_(*cols) → Ds

Returns a dataswim instance with a dataframe limited to some columns

Parameters:cols (str) – names of the columns Returns:a dataswim instance Return type:Ds Example:ds2 = ds.keep_("Col 1", "Col 2")

label = None

layout_(chart_objs, cols=3)

Returns a Holoview Layout from chart objects

limit(r: int = 5)

Limit selection to a range in the main dataframe

Parameters:r (int, optional) – number of rows to keep, defaults to 5

limit_(r: int = 5) → Ds

Returns a DataSwim instance with limited selection

Returns:a Ds instance Return type:Ds

line_(label=None, style=None, opts=None, options={})

Get a line chart

line_num_(label=None, style=None, opts=None)

Get an Altair line + number marks chart

line_point_(label=None, style=None, opts=None, options={}, colors={'line': 'orange', 'point': '#30A2DA'})

Get a line and point chart

load(table: str)

Set the main dataframe from a table’s data

Parameters:table (str) – table name Example:ds.load("mytable")

load_csv(url, **kwargs)

Loads csv data in the main dataframe

Parameters:

• url (str) – url of the csv file to load: can be absolute if it starts with / or relative if it starts with ./
• kwargs – keyword arguments to pass to Pandas read_csv function

Example:

ds.load_csv("./myfile.csv")

load_django(query: django query)

Load the main dataframe from a django orm query

Parameters:query (django query) – django query from a model Example:ds.load_django(Mymodel.objects.all())

load_django_(query: django query) → Ds

Returns a DataSwim instance from a django orm query

Parameters:query (django query) – django query from a model Returns:a dataswim instance with data from a django query Return type:Ds Example:ds2 = ds.load_django_(Mymodel.objects.all())

load_excel(filepath, **kwargs)

Set the main dataframe with the content of an Excel file

Parameters:

• filepath (str) – url of the csv file to load, can be absolute if it starts with / or relative if it starts with ./
• kwargs – keyword arguments to pass to Pandas read_excel function

Example:

ds.load_excel("./myfile.xlsx")

load_h5(filepath)

Load a Hdf5 file to the main dataframe

Parameters:filepath (str) – url of the csv file to load, can be absolute if it starts with / or relative if it starts with ./ Example:ds.load_h5("./myfile.hdf5")

load_json(path, **kwargs)

Load data in the main dataframe from json

Parameters:

• filepath (str) – url of the csv file to load, can be absolute if it starts with / or relative if it starts with ./
• kwargs – keyword arguments to pass to Pandas read_json function

Example:

ds.load_json("./myfile.json")

lreg(xcol, ycol, name='Regression')

Add a column to the main dataframe populted with the model’s linear regression for a column

lreg_(label=None, style=None, opts=None, options={})

Get a linear regression chart

map_(lat, long, zoom=13, tiles='map')

Returns a map

marker(lat, long, text, color=None, icon=None)

Set the main map with a marker to the default map

marker_(lat, long, text, pmap, color=None, icon=None)

Returns the map with a marker to the default map

mbar_(col, x=None, y=None, rsum=None, rmean=None)

Splits a column into multiple series based on the column’s unique values. Then visualize theses series in a chart. Parameters: column to split, x axis column, y axis column Optional: rsum=”1D” to resample and sum data an rmean=”1D” to mean the data

mcluster(lat_col: str, lon_col: str)

Add a markers cluster to the map

merge(df: pandas.core.frame.DataFrame, on: str, how: str = 'outer', **kwargs)

Set the main dataframe from the current dataframe and the passed dataframe

Parameters:

• df (pd.DataFrame) – the pandas dataframe to merge
• on (str) – param for pd.merge
• how (str, optional) – param for pd.merge, defaults to “outer”
• kwargs – keyword arguments for pd.merge

mfw_(col, sw_lang='english', limit=100)

Returns a Dataswim instance with the most frequent words in a column exluding the most common stop words

mline_(col, x=None, y=None, rsum=None, rmean=None)

Splits a column into multiple series based on the column’s unique values. Then visualize theses series in a chart. Parameters: column to split, x axis column, y axis column Optional: rsum=”1D” to resample and sum data an rmean=”1D” to mean the data

mline_point_(col, x=None, y=None, rsum=None, rmean=None)

Splits a column into multiple series based on the column’s unique values. Then visualize theses series in a chart. Parameters: column to split, x axis column, y axis column Optional: rsum=”1D” to resample and sum data an rmean=”1D” to mean the data

mpoint_(col, x=None, y=None, rsum=None, rmean=None)

Splits a column into multiple series based on the column’s unique values. Then visualize theses series in a chart. Parameters: column to split, x axis column, y axis column Optional: rsum=”1D” to resample and sum data an rmean=”1D” to mean the data

msg = None

msg_(label, *msg)

Returns a message with a label

nan = None

nan_empty(col: str)

Fill empty values with NaN values

Parameters:col (str) – name of the colum Example:ds.nan_empty("mycol")

ncontains(column, value)

Set the main dataframe instance to rows that do not contains a string value in a column

ndlayout_(dataset, kdims, cols=3)

Create a Holoview NdLayout from a dictionnary of chart objects

notebook = False

nowrange(col: str, timeframe: str)

Set the main dataframe with rows within a date range from now

Parameters:

• col (str) – the column to use for range
• timeframe (str) – units are: S, H, D, W, M, Y

example: ds.nowrange("Date", "3D")

nowrange_(col: str, timeframe: str) → Ds

Returns a Dataswim instance with rows within a date range from now

Parameters:

• col (str) – the column to use for range
• timeframe (str) – units are: S, H, D, W, M, Y

Returns:

[description]

Return type:

[type]

example: ds2 = ds.nowrange_("Date", "3D")

ok(*msg)

Prints a message with an ok prefix

one()

Shows one row of the dataframe and the field names wiht count

Returns:a pandas dataframe Return type:pd.DataFrame Example:ds.one()

opt(name, value)

Add or update one option

opts(dictobj)

Add or update options

pivot(index, **kwargs)

Pivots a dataframe

point_(label=None, style=None, opts=None, options={})

Get a point chart

point_num_(label=None, style=None, opts=None)

Get an Altair point + number marks chart

progress(*msg)

Prints a progress message

quants_(inf, sup, chart_type='point', color='green')

Draw a chart to visualize quantiles

query(q: str) → dataset.util.ResultIter

Query the database

Parameters:q (str) – the query to perform Returns:a dictionary with the query results Return type:dataset.util.ResultIter

quiet = False

radar_(label=None, style=None, opts=None, options={})

Get a radar chart

raenc(key)

Remove an entry from the altair encoding dict

raencs()

Reset the altair encoding dict

ratio(col: str, ratio_col: str = 'Ratio')

Add a column whith the percentages ratio from a column

Parameters:

• col (str) – column to calculate ratio from
• ratio_col – new ratio column name, defaults to “Ratio”
• ratio_col – str, optional

Example:

ds2 = ds.ratio("Col 1")

rcolor()

Reset the color to the base color

relation(table: str, origin_field: str, search_field: str, destination_field: str = None, id_field: str = 'id')

Add a column to the main dataframe from a relation foreign key

Parameters:

• table (str) – the table to select from
• origin_field (str) – the column name in the origin table to search from, generally an id column
• search_field (str) – the column name in the foreign table
• destination_field (str, optional) – name of the column to be created with the data in the datframe, defaults to None, will be named as the origin_field if not provided
• id_field (str, optional) – name of the primary key to use, defaults to “id”

example: ds.relation("product", "category_id", "name")

relation_(table: str, origin_field: str, search_field: str, destination_field=None, id_field='id') → pandas.core.frame.DataFrame

Returns a DataSwim instance with a column filled from a relation

foreign key

Parameters:

• table (str) – the table to select from
• origin_field (str) – the column name in the origin table to search from, generally an id column
• search_field (str) – the column name in the foreign table
• destination_field (str, optional) – name of the column to be created with the data in the datframe, defaults to None, will be named as the origin_field if not provided
• id_field (str, optional) – name of the primary key to use, defaults to “id”

Returns:

a pandas DataFrame

Return type:

DataFrame

rename(source_col: str, dest_col: str)

Renames a column in the main dataframe

Parameters:

• source_col (str) – name of the column to rename
• dest_col (str) – new name of the column

Example:

ds.rename("Col 1", "New col")

replace(col: str, searchval: str, replaceval: str)

Replace a value in a column in the main dataframe

Parameters:

• col (str) – column name
• searchval (str) – value to replace
• replaceval (str) – new value

Example:

ds.replace("mycol", "value", "new_value")

report_engines = []

report_path = None

reports = []

residual_(label=None, style=None, opts=None)

Returns a Seaborn models residuals chart

restore()

Restore the main dataframe

reverse()

Reverses the main dataframe order

Example:ds.reverse()

rmean(time_period: str, num_col: str = 'Number', dateindex: str = None)

Resample and add a mean column the main dataframe to a time period

Parameters:

• time_period – unit + period: periods are Y, M, D, H, Min, S
• time_period – str
• num_col – number of the new column, defaults to “Number”
• num_col – str, optional
• dateindex – column name to use as date index, defaults to None

Example:

ds.rmean("1Min")

rmean_(time_period: str, num_col: str = 'Number', dateindex: str = None)

Resample and add a mean column the main dataframe to a time period and returns a new Ds instance

Parameters:

• time_period – unit + period: periods are Y, M, D, H, Min, S
• time_period – str
• num_col – number of the new column, defaults to “Number”
• num_col – str, optional
• dateindex – column name to use as date index, defaults to None

Example:

ds.rmean_("1Min")

ropt(name)

Remove one option

ropts()

Reset the chart options

roundvals(col: str, precision: int = 2)

Round floats in a column. Numbers are going to be converted to floats if they are not already

Parameters:

• col (str) – column name
• precision – float precision, defaults to 2
• precision – int, optional

Example:

ds.roundvals("mycol")

rstyle(name)

Remove one style

rstyles()

Reset the chart options

rsum(time_period: str, num_col: str = 'Number', dateindex: str = None)

Resample and add a sum the main dataframe to a time period

Parameters:

• time_period – unit + period: periods are Y, M, D, H, Min, S
• time_period – str
• num_col – number of the new column, defaults to “Number”
• num_col – str, optional
• dateindex – column name to use as date index, defaults to None
• dateindex – str, optional

Example:

ds.rsum("1D")

rsum_(time_period: str, num_col: str = 'Number', dateindex: str = None)

Resample and add a sum the main dataframe to a time period and returns a new Ds instance

Parameters:

• time_period – unit + period: periods are Y, M, D, H, Min, S
• time_period – str
• num_col – number of the new column, defaults to “Number”
• num_col – str, optional
• dateindex – column name to use as date index, defaults to None
• dateindex – str, optional

Example:

ds.rsum_("1D")

rule_(label=None, style=None, opts=None, options={})

Get a rule chart

sarea_(col, x=None, y=None, rsum=None, rmean=None)

Get an stacked area chart

sbar_(stack_index=None, label=None, style=None, opts=None, options={})

Get a stacked bar chart

scolor()

Set a unique color from a serie

scommit()

sconnect(url: str)

seaborn_bar_(label=None, style=None, opts=None)

Get a Seaborn bar chart

show(rows: int = 5, dataframe: pandas.core.frame.DataFrame = None) → pandas.core.frame.DataFrame

Display info about the dataframe

Parameters:

• rows – number of rows to show, defaults to 5
• rows – int, optional
• dataframe – a pandas dataframe, defaults to None
• dataframe – pd.DataFrame, optional

Returns:

a pandas dataframe

Return type:

pd.DataFrame

Example:

ds.show()

size(val)

Change the chart’s point size

sline_(window_size=5, y_label='Moving average', chart_label=None)

Get a moving average curve chart to smooth between points

sort(col: str)

Sorts the main dataframe according to the given column

Parameters:col (str) – column name Example:ds.sort("Col 1")

split_(col: str) -> list(Ds)

Split the main dataframe according to a column’s unique values and return a dict of dataswim instances

Returns:list of dataswim instances Return type:list(Ds) Example:dss = ds.slit_("Col 1")

sq_(query: str)

sqm_(query: str, values)

square_(label=None, style=None, opts=None, options={})

Get a square chart

stack(slug, chart_obj=None, title=None)

Get the html for a chart and store it

start(*msg)

Prints an start message

start_time = None

static_path = None

status(*msg)

Prints a status message

strip(col: str)

Remove leading and trailing white spaces in a column’s values

Parameters:col (str) – name of the column Example:ds.strip("mycol")

strip_cols()

Remove leading and trailing white spaces in columns names

Example:ds.strip_cols()

style(name, value)

Add or update one style

styles(dictobj)

Add or update styles

subset(*args)

Set the main dataframe to a subset based in positions Select a subset of the main dataframe based on position: ex: ds.subset(0,10) or ds.subset(10) is equivalent: it starts at the first row if only one argument is provided

subset_(*args)

Returns a Dataswim instance with a subset data based in positions Select a subset of the main dataframe based on position: ex: ds.subset(0,10) or ds.subset(10) is equivalent: it starts at the first row if only one argument is provided

subtitle(txt)

Prints a subtitle for pipelines

sum_(col: str) → float

Returns the sum of all values in a column

Parameters:col (str) – column name Returns:sum of all the column values Return type:float Example:sum = ds.sum_("Col 1")

table(name: str)

Display info about a table: number of rows and columns

Parameters:name (str) – name of the table Example:tables = ds.table("mytable")

tables()

Print the existing tables in a database

Example:ds.tables()

tables_() → list

Return a list of the existing tables in a database

Returns:list of the table names Return type:list Example:tables = ds.tables_()

tail(rows: int = 5)

Returns the main dataframe’s tail

Parameters:

• rows – number of rows to print, defaults to 5
• rows – int, optional

Returns:

a pandas dataframe

Return type:

pd.DataFrame

Example:

ds.tail()

text_(label=None, style=None, opts=None)

Get an Altair text marks chart

tick_(label=None, style=None, opts=None, options={})

Get an tick chart

timestamps(col: str, **kwargs)

” Add a timestamps column from a date column

Parameters:

• col (str) – name of the timestamps column to add
• **kwargs (optional) – keyword arguments for pd.to_datetime

Example:

ds.timestamps("mycol")

title(txt)

Prints a title for pipelines

tmarker(lat, long, text, color=None, icon=None, style=None)

Returns the map with a text marker to the default map

tmarker_(lat, long, text, pmap, color=None, icon=None, style=None)

Returns the map with a text marker to the default map

to_csv(filepath: str, index: bool = False, **kwargs)

Write the main dataframe to a csv file

Parameters:

• filepath (str) – path of the file to save
• index – [description], defaults to False
• index – bool, optional
• *args – arguments to pass to pd.to_csv

Example:

ds.to_csv_("myfile.csv", header=false)

to_db(table: str, dtypes: List[sqlalchemy.sql.sqltypes.SchemaType] = None)

Save the main dataframe to the database

Parameters:

• table (str) – the table to create
• dtypes (List[SchemaType], optional) – SqlAlchemy columns type, defaults to None, will be infered if not provided

to_excel(filepath: str, title: str)

Write the main dataframe to an Excell file

Parameters:

• filepath (str) – path of the Excel file to write
• title (str) – Title of the stylesheet

Example:

ds.to_excel_("./myfile.xlsx", "My data")

to_file(slug, folderpath=None, header=None, footer=None)

Writes the html report to a file from the report stack

to_files(folderpath=None)

Writes the html report to one file per report

to_float(col: str, **kwargs)

Convert colums values to float

Parameters:

• col (str, at least one) – name of the colum
• **kwargs (optional) – keyword arguments for df.astype

Example:

ds.to_float("mycol1")

to_hdf5(filepath: str)

Write the main dataframe to Hdf5 file

Parameters:filepath (str) – path where to save the file Example:ds.to_hdf5_("./myfile.hdf5")

to_html_() → str

Convert the main dataframe to html

Returns:html data Return type:str Example:ds.to_html_()

to_int(*cols, **kwargs)

Convert some column values to integers

Parameters:

• *cols (str, at least one) – names of the colums
• **kwargs (optional) – keyword arguments for pd.to_numeric

Example:

ds.to_int("mycol1", "mycol2", errors="coerce")

to_javascript_(table_name: str = 'data') → str

Convert the main dataframe to javascript code

Parameters:

• table_name – javascript variable name, defaults to “data”
• table_name – str, optional

Returns:

a javascript constant with the data

Return type:

str

Example:

ds.to_javastript_("myconst")

to_json_() → str

Convert the main dataframe to json

Returns:json data Return type:str Example:ds.to_json_()

to_markdown_() → str

Convert the main dataframe to markdown

Returns:markdown data Return type:str Example:ds.to_markdown_()

to_numpy_(table_name: str = 'data') → numpy.array

Convert the main dataframe to a numpy array

Parameters:

• table_name – name of the python variable, defaults to “data”
• table_name – str, optional

Returns:

a numpy array

Return type:

np.array

Example:

ds.to_numpy_("myvar")

to_python_(table_name: str = 'data') → list

Convert the main dataframe to python a python list

Parameters:

• table_name – python variable name, defaults to “data”
• table_name – str, optional

Returns:

a python list of lists with the data

Return type:

str

Example:

ds.to_python_("myvar")

to_records_() → dict

Returns a list of dictionary records from the main dataframe

Returns:a python dictionnary with the data Return type:str Example:ds.to_records_()

to_rst_() → str

Convert the main dataframe to restructured text

Returns:rst data Return type:str Example:ds.to_rst_()

to_type(dtype: type, *cols, **kwargs)

Convert colums values to a given type in the main dataframe

Parameters:

• dtype (type) – a type to convert to: ex: str
• *cols (str, at least one) – names of the colums
• **kwargs (optional) – keyword arguments for df.astype

Example:

ds.to_type(str, "mycol")

trimiquants(col: str, inf: float)

Remove superior and inferior quantiles from the dataframe

Parameters:

• col (str) – column name
• inf (float) – inferior quantile

Example:

ds.trimiquants("Col 1", 0.05)

trimquants(col: str, inf: float, sup: float)

Remove superior and inferior quantiles from the dataframe

Parameters:

• col (str) – column name
• inf (float) – inferior quantile
• sup (float) – superior quantile

Example:

ds.trimquants("Col 1", 0.01, 0.99)

trimsquants(col: str, sup: float)

Remove superior quantiles from the dataframe

Parameters:

• col (str) – column name
• sup (float) – superior quantile

Example:

ds.trimsquants("Col 1", 0.99)

types_(col: str) → pandas.core.frame.DataFrame

Display types of values in a column

Parameters:col (str) – column name Returns:a pandas dataframe Return type:pd.DataFrame Example:ds.types_("Col 1")

unique_(col: str) → list

Returns unique values in a column

Parameters:col (str) – the column to select from Returns:a list of unique values in the column Return type:list

update_table(table: str, pks: List[str] = ['id'], mirror: bool = True)

Update records in a database table from the main dataframe

Parameters:

• table (str) – table to update
• pks (List[str], optional :param mirror: delete the rows not in the new datataset) – if rows with matching pks exist they will be updated, otherwise a new row is inserted in the table, defaults to [“id”]

upsert(table: str, record: dict, create_cols: bool = False, dtypes: List[sqlalchemy.sql.sqltypes.SchemaType] = None, pks: List[str] = ['id'])

Upsert a record in a table

Parameters:

• table (str) – the table to upsert into
• record (dict) – dictionary with the data to upsert
• create_cols (bool, optional) – create the columns if it doesn’t exist, defaults to False
• dtypes (List[SchemaType], optional) – list of SqlAlchemy column types, defaults to None
• pks (List[str], optional) – if rows with matching pks exist they will be updated, otherwise a new row is inserted in the table, defaults to [“id”]

version = '0.6.0'

warning(*msg)

Prints a warning

width(val)

Change the chart’s width

wunique_(col)

Weight unique values: returns a dataframe with a count of unique values

x = None

y = None

zero_nan(*cols)

Converts zero values to nan values in selected columns

Parameters:*cols (str, at least one) – names of the colums Example:ds.zero_nan("mycol1", "mycol2")