dtale

 # conda
conda install dtale -c conda-forge
# if you want to also use "Export to PNG" for charts
conda install -c plotly python-kaleido

 # or PyPI
pip install dtale 

dtale import pandas as pd df = pd.DataFrame([dict(a=1,b=2,c=3)]) # Assigning a reference to a running D-Tale process. d = dtale .show(df) # Accessing data associated with D-Tale process. tmp = d.data.copy() tmp['d'] = 4 # Altering data associated with D-Tale process # FYI: this will clear any front-end settings you have at the time for this process (filter, sorts, formatting) d.data = tmp # Get raw dataframe w/ any sorting or edits made through the UI d.data # Get raw dataframe similar to '.data' along with any filters applied using the UI d.view_data # Shutting down D-Tale process d.kill() # Using Python's `webbrowser` package it will try and open your server's default browser to this process. d.open_browser() # There is also some helpful metadata about the process. d._data_id # The process's data identifier. d._url # The url to access the process. d2 = dtale .get_instance(d._data_id) # Returns a new reference to the instance running at that data_id. dtale .instances() # Prints a list of all ids & urls of running D-Tale sessions. ">

 import dtale
import pandas as pd

df = pd . DataFrame ([ dict ( a = 1 , b = 2 , c = 3 )])

# Assigning a reference to a running D-Tale process.
d = dtale . show ( df )

# Accessing data associated with D-Tale process.
tmp = d . data . copy ()
tmp [ 'd' ] = 4

# Altering data associated with D-Tale process
# FYI: this will clear any front-end settings you have at the time for this process (filter, sorts, formatting)
d . data = tmp

# Get raw dataframe w/ any sorting or edits made through the UI
d . data

# Get raw dataframe similar to '.data' along with any filters applied using the UI
d . view_data

# Shutting down D-Tale process
d . kill ()

# Using Python's `webbrowser` package it will try and open your server's default browser to this process.
d . open_browser ()

# There is also some helpful metadata about the process.
d . _data_id  # The process's data identifier.
d . _url  # The url to access the process.

d2 = dtale . get_instance ( d . _data_id )  # Returns a new reference to the instance running at that data_id.

dtale . instances ()  # Prints a list of all ids & urls of running D-Tale sessions. 

dtale import pandas as pd if __name__ == '__main__': dtale .show(pd.DataFrame([1,2,3,4,5]), subprocess=False)">

 import dtale
import pandas as pd

if __name__ == '__main__' :
      dtale . show ( pd . DataFrame ([ 1 , 2 , 3 , 4 , 5 ]), subprocess = False )

dtale import dtale .app as dtale _app dtale _app.JUPYTER_SERVER_PROXY = True dtale .show(pd.DataFrame([1,2,3]))">

 import pandas as pd

import dtale
import dtale . app as dtale _app

dtale _app . JUPYTER_SERVER_PROXY = True

dtale . show ( pd . DataFrame ([ 1 , 2 , 3 ]))

dtale import dtale .app as dtale _app dtale _app.JUPYTER_SERVER_PROXY = True d = dtale .show(pd.DataFrame([1,2,3])) d._main_url # /user/johndoe/proxy/40000/ dtale /main/1">

 import pandas as pd

import dtale
import dtale . app as dtale _app

dtale _app . JUPYTER_SERVER_PROXY = True

d = dtale . show ( pd . DataFrame ([ 1 , 2 , 3 ]))
d . _main_url # /user/johndoe/proxy/40000/ dtale /main/1

dtale import dtale .app as dtale _app dtale .show(pd.DataFrame([1,2,3]), app_root='/user/johndoe/proxy/40000/`)">

 import pandas as pd

import dtale
import dtale . app as dtale _app

dtale . show ( pd . DataFrame ([ 1 , 2 , 3 ]), app_root = ' / user / johndoe / proxy / 40000 / ` )

docker run -h ` hostname ` -p 40000:40000

docker run -p 40000:40000

docker run -h ` hostname ` --network host

dtale import dtale .app as dtale _app dtale _app.USE_COLAB = True dtale .show(pd.DataFrame([1,2,3]))">

 import pandas as pd

import dtale
import dtale . app as dtale _app

dtale _app . USE_COLAB = True

dtale . show ( pd . DataFrame ([ 1 , 2 , 3 ]))

dtale import dtale .app as dtale _app dtale _app.USE_NGROK = True dtale .show(pd.DataFrame([1,2,3]))">

 import pandas as pd

import dtale
import dtale . app as dtale _app

dtale _app . USE_NGROK = True

dtale . show ( pd . DataFrame ([ 1 , 2 , 3 ]))

library('reticulate')
dtale <- import(' dtale ')
df <- read.csv('https://vincentarelbundock.g*ith*u*b.io/Rdatasets/csv/boot/acme.csv')
dtale $show(df, subprocess=FALSE, open_browser=TRUE)

 dtale --arcticdb-uri lmdb:/// < path > --arcticdb-library jdoe.my_lib --arcticdb-symbol my_symbol

 dtale --arcticdb-uri lmdb:/// < path > --arcticdb-library my_lib --arcticdb-symbol my_symbol --arcticdb-use_store

 dtale --arctic-uri mongodb://localhost:27027 --arctic-library my_lib --arctic-symbol my_symbol --arctic-start 20130101 --arctic-end 20161231

 dtale --csv-path /home/jdoe/my_csv.csv --csv-parse_dates date

 dtale --excel-path /home/jdoe/my_csv.xlsx --excel-parse_dates date
dtale --excel-path /home/jdoe/my_csv.xls --excel-parse_dates date

 dtale --json-path /home/jdoe/my_json.json --json-parse_dates date

dtale --json-path http://*json-*e*ndpoint --json-parse_dates date

 dtale --r-path /home/jdoe/my_dataset.rda

 dtale --sqlite-path /home/jdoe/test.sqlite3 --sqlite-table test_table

dtale.cli.clickutils import get_loader_options ''' IMPORTANT!!! This global variable is required for building any customized CLI loader. When find loaders on startup it will search for any modules containing the global variable LOADER_KEY. ''' LOADER_KEY = 'testdata' LOADER_PROPS = ['rows', 'columns'] def test_data(rows, columns): import pandas as pd import numpy as np import random from past.utils import old_div from pandas.tseries.offsets import Day from dtale .utils import dict_merge import string now = pd.Timestamp(pd.Timestamp('now').date()) dates = pd.date_range(now - Day(364), now) num_of_securities = max(old_div(rows, len(dates)), 1) # always have at least one security securities = [ dict(security_id=100000 + sec_id, int_val=random.randint(1, 100000000000), str_val=random.choice(string.ascii_letters) * 5) for sec_id in range(num_of_securities) ] data = pd.concat([ pd.DataFrame([dict_merge(dict(date=date), sd) for sd in securities]) for date in dates ], ignore_index=True)[['date', 'security_id', 'int_val', 'str_val']] col_names = ['Col{}'.format(c) for c in range(columns)] return pd.concat([data, pd.DataFrame(np.random.randn(len(data), columns), columns=col_names)], axis=1) # IMPORTANT!!! This function is required for building any customized CLI loader. def find_loader(kwargs): test_data_opts = get_loader_options(LOADER_KEY, LOADER_PROPS, kwargs) if len([f for f in test_data_opts.values() if f]): def _testdata_loader(): return test_data(int(test_data_opts.get('rows', 1000500)), int(test_data_opts.get('columns', 96))) return _testdata_loader return None">

 from dtale . cli . clickutils import get_loader_options

'''
  IMPORTANT!!! This global variable is required for building any customized CLI loader.
  When find loaders on startup it will search for any modules containing the global variable LOADER_KEY.
'''
LOADER_KEY = 'testdata'
LOADER_PROPS = [ 'rows' , 'columns' ]


def test_data ( rows , columns ):
    import pandas as pd
    import numpy as np
    import random
    from past . utils import old_div
    from pandas . tseries . offsets import Day
    from dtale . utils import dict_merge
    import string

    now = pd . Timestamp ( pd . Timestamp ( 'now' ). date ())
    dates = pd . date_range ( now - Day ( 364 ), now )
    num_of_securities = max ( old_div ( rows , len ( dates )), 1 )  # always have at least one security
    securities = [
        dict ( security_id = 100000 + sec_id , int_val = random . randint ( 1 , 100000000000 ),
             str_val = random . choice ( string . ascii_letters ) * 5 )
        for sec_id in range ( num_of_securities )
    ]
    data = pd . concat ([
        pd . DataFrame ([ dict_merge ( dict ( date = date ), sd ) for sd in securities ])
        for date in dates
    ], ignore_index = True )[[ 'date' , 'security_id' , 'int_val' , 'str_val' ]]

    col_names = [ 'Col{}' . format ( c ) for c in range ( columns )]
    return pd . concat ([ data , pd . DataFrame ( np . random . randn ( len ( data ), columns ), columns = col_names )], axis = 1 )


# IMPORTANT!!! This function is required for building any customized CLI loader.
def find_loader ( kwargs ):
    test_data_opts = get_loader_options ( LOADER_KEY , LOADER_PROPS , kwargs )
    if len ([ f for f in test_data_opts . values () if f ]):
        def _testdata_loader ():
            return test_data ( int ( test_data_opts . get ( 'rows' , 1000500 )), int ( test_data_opts . get ( 'columns' , 96 )))

        return _testdata_loader
    return None

 dtale _CLI_LOADERS=./path_to_loaders bash -c ' dtale --testdata-rows 10 --testdata-columns 5 '

 [auth]
active = True
username = johndoe
password = 1337h4xOr

dtale.global_state as global_state global_state.set_auth_settings({'active': True, 'username': 'johndoe', 'password': '1337h4x0r'})">

 import dtale . global_state as global_state

global_state . set_auth_settings ({ 'active' : True , 'username' : 'johndoe' , 'password' : '1337h4x0r' })

dtale import pandas as pd df = pd.DataFrame(dict( a=[1,2,3,4,5], b=[6,7,8,9,10], c=['a','b','c','d','e'] )) dtale .show( df, locked=['c'], column_formats={'a': {'fmt': '0.0000'}}, nan_display='...', background_mode='heatmap-col', sort=[('a','DESC')], vertical_headers=True, )">

 import dtale
import pandas as pd

df = pd . DataFrame ( dict (
  a = [ 1 , 2 , 3 , 4 , 5 ],
  b = [ 6 , 7 , 8 , 9 , 10 ],
  c = [ 'a' , 'b' , 'c' , 'd' , 'e' ]
))
dtale . show (
  df ,
  locked = [ 'c' ],
  column_formats = { 'a' : { 'fmt' : '0.0000' }},
  nan_display = '...' ,
  background_mode = 'heatmap-col' ,
  sort = [( 'a' , 'DESC' )],
  vertical_headers = True ,
)

dtale import pandas as pd df = pd.DataFrame(dict( a=[1,2,3,4,5], b=[6,7,8,9,10], c=['a','b','c','d','e'] )) d = dtale .show( df ) d.update_settings( locked=['c'], column_formats={'a': {'fmt': '0.0000'}}, nan_display='...', background_mode='heatmap-col', sort=[('a','DESC')], vertical_headers=True, ) d">

 import dtale
import pandas as pd

df = pd . DataFrame ( dict (
  a = [ 1 , 2 , 3 , 4 , 5 ],
  b = [ 6 , 7 , 8 , 9 , 10 ],
  c = [ 'a' , 'b' , 'c' , 'd' , 'e' ]
))
d = dtale . show (
  df
)
d . update_settings (
  locked = [ 'c' ],
  column_formats = { 'a' : { 'fmt' : '0.0000' }},
  nan_display = '...' ,
  background_mode = 'heatmap-col' ,
  sort = [( 'a' , 'DESC' )],
  vertical_headers = True ,
)
d

'a': {
  'active': True,
  'equals': {'active': True, 'value': 3, 'color': {'r': 255, 'g': 245, 'b': 157, 'a': 1}}, # light yellow
  'greaterThan': {'active': True, 'value': 3, 'color': {'r': 80, 'g': 227, 'b': 194, 'a': 1}}, # mint green
  'lessThan': {'active': True, 'value': 3, 'color': {'r': 245, 'g': 166, 'b': 35, 'a': 1}}, # orange
}

dtale import dtale .predefined_filters as predefined_filters import dtale .global_state as global_state global_state.set_app_settings(dict(open_predefined_filters_on_startup=True)) predefined_filters.set_filters([ { "name": "A and B > 2", "column": "A", "description": "Filter A with B greater than 2", "handler": lambda df, val: df[(df["A"] == val) & (df["B"] > 2)], "input_type": "input", "default": 1, "active": False, }, { "name": "A and (B % 2) == 0", "column": "A", "description": "Filter A with B mod 2 equals zero (is even)", "handler": lambda df, val: df[(df["A"] == val) & (df["B"] % 2 == 0)], "input_type": "select", "default": 1, "active": False, }, { "name": "A in values and (B % 2) == 0", "column": "A", "description": "A is within a group of values and B mod 2 equals zero (is even)", "handler": lambda df, val: df[df["A"].isin(val) & (df["B"] % 2 == 0)], "input_type": "multiselect", "default": [1], "active": True, } ]) df = pd.DataFrame( ([[1, 2, 3, 4, 5, 6], [7, 8, 9, 10, 11, 12], [13, 14, 15, 16, 17, 18]]), columns=['A', 'B', 'C', 'D', 'E', 'F'] ) dtale .show(df)">

 import pandas as pd
import dtale
import dtale . predefined_filters as predefined_filters
import dtale . global_state as global_state

global_state . set_app_settings ( dict ( open_predefined_filters_on_startup = True ))

predefined_filters . set_filters ([
    {
        "name" : "A and B > 2" ,
        "column" : "A" ,
        "description" : "Filter A with B greater than 2" ,
        "handler" : lambda df , val : df [( df [ "A" ] == val ) & ( df [ "B" ] > 2 )],
        "input_type" : "input" ,
        "default" : 1 ,
        "active" : False ,
    },
    {
        "name" : "A and (B % 2) == 0" ,
        "column" : "A" ,
        "description" : "Filter A with B mod 2 equals zero (is even)" ,
        "handler" : lambda df , val : df [( df [ "A" ] == val ) & ( df [ "B" ] % 2 == 0 )],
        "input_type" : "select" ,
        "default" : 1 ,
        "active" : False ,
    },
    {
        "name" : "A in values and (B % 2) == 0" ,
        "column" : "A" ,
        "description" : "A is within a group of values and B mod 2 equals zero (is even)" ,
        "handler" : lambda df , val : df [ df [ "A" ]. isin ( val ) & ( df [ "B" ] % 2 == 0 )],
        "input_type" : "multiselect" ,
        "default" : [ 1 ],
        "active" : True ,
    }
])

df = pd . DataFrame (
    ([[ 1 , 2 , 3 , 4 , 5 , 6 ], [ 7 , 8 , 9 , 10 , 11 , 12 ], [ 13 , 14 , 15 , 16 , 17 , 18 ]]),
    columns = [ 'A' , 'B' , 'C' , 'D' , 'E' , 'F' ]
)
dtale . show ( df )

pip install swifter
# or
pip install dtale [swifter]

dtale dtale .show(pd.DataFrame( {'col{}'.format(i): list(range(1000)) for i in range(105)} ))">

 import pandas as pd
import dtale

dtale . show ( pd . DataFrame (
    { 'col{}' . format ( i ): list
					
					

							
下载源码
						

							

															从 GitHub 克隆
								
								
							
						
												

							
通过命令行克隆项目:
							

								git clone https://github.com/man-group/dtale.git