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import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
import matplotlib.patches as mpatches
%matplotlib inline
import seaborn as sns
import calendar
import plotly as pt
from plotly import graph_objs as go
import plotly.express as px
import plotly.figure_factory as ff
from pylab import *
import matplotlib.patheffects as PathEffects
import dask.dataframe as dd


import warnings
warnings.filterwarnings('ignore')
datafile = './Data/US_Accidents_March23.csv'

Data Preparation and Cleaning

  1. Load the file using Pandas
  2. Look at some information about the data & the columns
  3. Fix any missing or incorrect values
%%time
df = dd.read_csv(datafile)
Wall time: 23.4 ms 
%%time
df = df.compute()
Wall time: 1min 57s