Exploring Binary and Categorical Data¶

In [1]:

import pandas as pd

import pandas as pd

In [2]:

data = pd.read_csv("../data/dfw_airline.csv")
data

data = pd.read_csv("../data/dfw_airline.csv") data

Out[2]:

	Carrier	ATC	Weather	Security	Inbound
0	64263.16	84856.5	11235.42	343.15	118427.82

In [3]:

flight = data.transpose()
flight.plot.bar(legend=False, figsize=(4,4))

flight = data.transpose() flight.plot.bar(legend=False, figsize=(4,4))

Out[3]:

<Axes: >

In [4]:

flight_with_melt = data.melt(var_name="Cause", value_name="Count")
flight_with_melt.sort_values("Count", ascending=False, inplace=True)

flight_with_melt = data.melt(var_name="Cause", value_name="Count") flight_with_melt.sort_values("Count", ascending=False, inplace=True)

In [5]:

flight_with_melt.plot.bar(x="Cause", y="Count", legend=True, figsize=(4,4))

flight_with_melt.plot.bar(x="Cause", y="Count", legend=True, figsize=(4,4))

Out[5]:

<Axes: xlabel='Cause'>

We can see that the mode of the cause of deleay for this dataset is Inbound.

Further Reading¶

• Misleading graphs