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How to use python relative imports

Don’t. They are tempting, but they are a really big mess. Let’s start by figuring out what you’re really trying to accomplish. Let’s start with a directory structure.

/Grand-

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window.sumClasses = () ->
  c_set = {}
  $("*").each((count, el) ->
    window.el = el
    clist = []
    _.each(el.classList, (cname) ->
      clist.push(cname))
      
      

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SELECT
        tas.start_station_id AS start_id, tas.end_station_id AS end_id,
        ROUND(tas.avg_taxi_time,2) AS avg_taxi, ROUND(cas.cb_trip_duration,2) AS avg_cb, 
        ROUND(cas.cb_trip_duration -tas.avg_taxi_time,  2) AS diff,
        tas.tt_trip_count AS tt_cnt, cas.cb_trip_count AS cb_cnt,
        'https://www.google.com/maps/dir/' || cs1.latitude || ','|| cs1.longitude || '/' ||  cs2.latitude ||','|| cs2.longitude
FROM
        citibike_agg_stat AS cas,
        taxi_agg_stat AS tas,

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import matplotlib
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.widgets import Slider, Button, RadioButtons

class PlotController(object):
    def __init__(self):
        self.figs = []

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"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var FlatBush = require("flatbush");
var bbox_1 = require("./bbox");
var SpatialIndex = /** @class */ (function () {
    function SpatialIndex(points) {
        this.points = points;
        this.index = null;
        if (points.length > 0) {
            this.index = new FlatBush(points.length);

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(defmacro hook-add-or-update (hook fname &rest body)
  "Macro to make defining and updating hooks much easier.
   Hook functions get an fname.
   Before defining and adding a hook to a list, macro first checks if that fname is already defined, if so, the old version is removed from hook

  used as follows
  (hook-add-or-update
     shell-mode-hook fourth-shell-hook
     (message \"macro fourth rev 2\"))"
  

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With the new release of buckaroo, df.head() is obsolete. I have worked to make Buckaroo usable as the default table visualization for pandas dataframes. It does this through sensible defaults and down sampling. The default process of investigating a new dataset with pandas and jupyter is to load a dataframe from csv, parquet, or some other data source. The next step is df.head() or df.describe(), if you just type df pandas will try to show the first 5 rows and last 5 rows, and possibly all of the columns. Pandas needs to limit the output to avoid overwhelming a notebook with text output, and causing performance issues. Soon you will find yourself looking up pd.options.display.width = 0 or pd.options.display.max_rows = 500

with pd.option_context('display.max_rows', None, 'display.max_columns', None):
 print (df)

Eventually you will want to look at a subset of rows, using slicing. Looking up sorting… How do I find the rows with the highest or lowest values in a column you could use some

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sample gist