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Pandas + matplotlib

import pandas as pd
import matplotlib.pyplot as plt
titanic = pd.read_csv("data/titanic.csv")
titanic["age"] = pd.to_numeric(titanic["age"], errors="coerce")
houses = pd.read_csv("data/kc_house_data.csv")
ufos = pd.read_csv("data/nuforc_reports.csv", parse_dates=['date_time', "posted"])
titanic.sex.value_counts()
sex male 843 female 466 Name: count, dtype: int64
titanic.sex.value_counts().plot(kind="bar")
<Axes: xlabel='sex'>
<Figure size 640x480 with 1 Axes>
houses[["sqft_living", "sqft_lot"]].sort_values("sqft_living", ascending=False).head(10)
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houses[["sqft_living", "sqft_lot"]].sort_values("sqft_living", ascending=False).head(10).plot(kind="bar")
<Axes: >
<Figure size 640x480 with 1 Axes>
plt.style.available
['Solarize_Light2', '_classic_test_patch', '_mpl-gallery', '_mpl-gallery-nogrid', 'bmh', 'classic', 'dark_background', 'fast', 'fivethirtyeight', 'ggplot', 'grayscale', 'petroff10', 'seaborn-v0_8', 'seaborn-v0_8-bright', 'seaborn-v0_8-colorblind', 'seaborn-v0_8-dark', 'seaborn-v0_8-dark-palette', 'seaborn-v0_8-darkgrid', 'seaborn-v0_8-deep', 'seaborn-v0_8-muted', 'seaborn-v0_8-notebook', 'seaborn-v0_8-paper', 'seaborn-v0_8-pastel', 'seaborn-v0_8-poster', 'seaborn-v0_8-talk', 'seaborn-v0_8-ticks', 'seaborn-v0_8-white', 'seaborn-v0_8-whitegrid', 'tableau-colorblind10']
plt.style.use("fivethirtyeight")
titanic.sex.value_counts().plot(kind="bar")
<Axes: xlabel='sex'>
<Figure size 640x480 with 1 Axes>
titanic.sex.value_counts().plot(kind="bar", title="Sex Breakdown On Titanic")
<Axes: title={'center': 'Sex Breakdown On Titanic'}, xlabel='sex'>
<Figure size 640x480 with 1 Axes>
titanic.sex.value_counts().plot(kind="bar")
plt.title("My Title", loc="right")
<Figure size 640x480 with 1 Axes>
ufos["month"] = ufos["date_time"].dt.month
ufos["year"] = ufos["date_time"].dt.year
sightings = ufos.month.value_counts().sort_index()
sightings.plot(
    kind="bar", 
    title="UFO Sightings By Month", 
    xlabel="Month",
    ylabel="Num Sightings",
    fontsize="10")
<Axes: title={'center': 'UFO Sightings By Month'}, xlabel='Month', ylabel='Num Sightings'>
<Figure size 640x480 with 1 Axes>
sightings = ufos.month.value_counts().sort_index()
sightings.plot(
    kind="bar", 
    title="UFO Sightings By Month", 
    fontsize="10")
plt.xlabel("Month", fontsize=24, color="olive")
plt.ylabel("Num Sightings", fontsize=24)
<Figure size 640x480 with 1 Axes>
sightings = ufos.month.value_counts().sort_index()
sightings.plot(
    kind="bar", 
    title="UFO Sightings By Month", 
    fontsize="10")
plt.xlabel("Month", fontsize=24, color="olive")
plt.ylabel("Num Sightings", fontsize=24)
<Figure size 640x480 with 1 Axes>
sightings
month 1.0 5979 2.0 4559 3.0 5494 4.0 5817 5.0 6063 6.0 8357 7.0 10682 8.0 8997 9.0 8498 10.0 8371 11.0 7596 12.0 6525 Name: count, dtype: int64
months_dict = {1: "Jan", 2: "Feb", 3: "Mar", 4: "Apr", 5: "May", 6: "Jun", 7:"Jul", 8: "Aug", 9:"Sep", 10: "Oct", 11: "Nov", 12: "Dec"}
sightings.rename(months_dict)
month Jan 5979 Feb 4559 Mar 5494 Apr 5817 May 6063 Jun 8357 Jul 10682 Aug 8997 Sep 8498 Oct 8371 Nov 7596 Dec 6525 Name: count, dtype: int64
sightings.rename(months_dict).plot(
    kind="bar", 
    title="UFO Sightings By Month", 
    fontsize="20")
plt.xlabel("Month", fontsize=24, color="olive")
plt.ylabel("Num Sightings", fontsize=24)
<Figure size 640x480 with 1 Axes>
salaries = pd.read_csv("data/Salaries.csv", low_memory=False)
salaries["BasePay"] = pd.to_numeric(salaries["BasePay"], errors="coerce").fillna(0)
salaries["OvertimePay"] = pd.to_numeric(salaries["OvertimePay"], errors="coerce").fillna(0)
salaries["OtherPay"] = pd.to_numeric(salaries["OtherPay"], errors="coerce").fillna(0)
df = salaries[["EmployeeName", "BasePay", "OvertimePay", "OtherPay"]]
df.set_index("EmployeeName", inplace=True)
df.head(10).plot(kind="bar")
<Axes: xlabel='EmployeeName'>
<Figure size 640x480 with 1 Axes>
df.head(10).plot(kind="bar", stacked=True)
<Axes: xlabel='EmployeeName'>
<Figure size 640x480 with 1 Axes>
df["BasePay"].sort_values(ascending=False).head().plot(kind="bar")
<Axes: xlabel='EmployeeName'>
<Figure size 640x480 with 1 Axes>
df["BasePay"].sort_values(ascending=False).head().plot.bar(color="red", title="Highest Paid SF Employees")
<Axes: title={'center': 'Highest Paid SF Employees'}, xlabel='EmployeeName'>
<Figure size 640x480 with 1 Axes>
df["BasePay"].sort_values(ascending=False).head().plot.barh(color="red", title="Highest Paid SF Employees")
<Axes: title={'center': 'Highest Paid SF Employees'}, ylabel='EmployeeName'>
<Figure size 640x480 with 1 Axes>
df.sort_values("OvertimePay", ascending=False).head().plot(kind="barh", stacked=True)
<Axes: ylabel='EmployeeName'>
<Figure size 640x480 with 1 Axes>
df.sort_values("OvertimePay", ascending=False).head().plot.barh(stacked=True)
plt.xlabel("Dollars")
<Figure size 640x480 with 1 Axes>
df["BasePay"].plot(kind="hist", bins=200, color="purple")
<Axes: ylabel='Frequency'>
<Figure size 640x480 with 1 Axes>
df.plot(kind="hist", bins=100)
<Axes: ylabel='Frequency'>
<Figure size 640x480 with 1 Axes>
df.plot.hist(title="SF Salaries")
<Axes: title={'center': 'SF Salaries'}, ylabel='Frequency'>
<Figure size 640x480 with 1 Axes>
df.hist()
array([[<Axes: title={'center': 'BasePay'}>, <Axes: title={'center': 'OvertimePay'}>], [<Axes: title={'center': 'OtherPay'}>, <Axes: >]], dtype=object)
<Figure size 640x480 with 4 Axes>

Box Plots

df.plot(kind="box")
<Axes: >
<Figure size 640x480 with 1 Axes>
df.boxplot()
<Axes: >
<Figure size 640x480 with 1 Axes>
houses.bedrooms.plot(kind="box", showfliers=False)
<Axes: >
<Figure size 640x480 with 1 Axes>
houses[["bedrooms", "bathrooms"]].boxplot(showfliers=False)
<Axes: >
<Figure size 640x480 with 1 Axes>

Line Plots

ufos.year.value_counts().sort_index().plot(kind="line", color="olive", linestyle=":")
<Axes: xlabel='year'>
<Figure size 640x480 with 1 Axes>
houses.plot.line(x="bedrooms", y="price")
<Axes: xlabel='bedrooms'>
<Figure size 640x480 with 1 Axes>

Scatter Plots

houses.plot.scatter(x="bedrooms", y="bathrooms")
<Axes: xlabel='bedrooms', ylabel='bathrooms'>
<Figure size 640x480 with 1 Axes>
houses.plot.scatter(x="bedrooms", y="bathrooms", marker="X")
<Axes: xlabel='bedrooms', ylabel='bathrooms'>
<Figure size 640x480 with 1 Axes>
ufos[ufos["state"] == "CA"].year.value_counts().sort_index().plot(label="CA")
ufos[ufos["state"] == "TX"].year.value_counts().sort_index().plot(label="TX")
plt.legend(loc="upper left")
plt.title("UFO Sighting By State")
plt.xlabel("Year")
<Figure size 640x480 with 1 Axes>
ufos["shape"].value_counts()
shape light 18230 circle 10130 triangle 7910 fireball 7095 unknown 6182 sphere 6065 other 5749 disk 4522 oval 3857 formation 2842 changing 2112 cigar 1990 flash 1675 rectangle 1597 cylinder 1374 diamond 1273 chevron 953 teardrop 767 egg 666 cone 349 cross 289 Name: count, dtype: int64
ufos[ufos["shape"] == "formation"].year.value_counts().sort_index().plot(label="formation")
ufos[ufos["shape"] == "oval"].year.value_counts().sort_index().plot(label="oval")
# ufos[ufos["shape"] == "light"].year.value_counts().sort_index().plot(label="light")
# ufos[ufos["shape"] == "circle"].year.value_counts().sort_index().plot(label="circle")
# ufos[ufos["shape"] == "fireball"].year.value_counts().sort_index().plot(label="fireball")
ufos[ufos["shape"] == "disk"].year.value_counts().sort_index().plot(label="disk")
plt.xlim(2000,2020)
plt.legend(loc="upper left")
plt.title("UFO Sighting By State")
plt.xlabel("Year")
<Figure size 640x480 with 1 Axes>
ufos[ufos["shape"] == "formation"].year.value_counts().sort_index().plot(label="formation")
ufos[ufos["shape"] == "oval"].year.value_counts().sort_index().plot(label="oval")
ufos[ufos["shape"] == "light"].year.value_counts().sort_index().plot(label="light")
# ufos[ufos["shape"] == "circle"].year.value_counts().sort_index().plot(label="circle")
ufos[ufos["shape"] == "fireball"].year.value_counts().sort_index().plot(label="fireball")
ufos[ufos["shape"] == "disk"].year.value_counts().sort_index().plot(label="disk")
# plt.xlim(2000,2020)
plt.legend(loc="upper left")
plt.title("UFO Sighting By State")
plt.xlabel("Year")
<Figure size 640x480 with 1 Axes>
ufos[ufos["shape"] == "formation"].year.value_counts().sort_index().plot(label="formation")
ufos[ufos["shape"] == "oval"].year.value_counts().sort_index().plot(label="oval")
ufos[ufos["shape"] == "light"].year.value_counts().sort_index().plot(label="light")
# ufos[ufos["shape"] == "circle"].year.value_counts().sort_index().plot(label="circle")
ufos[ufos["shape"] == "fireball"].year.value_counts().sort_index().plot(label="fireball")
ufos[ufos["shape"] == "disk"].year.value_counts().sort_index().plot(label="disk")
# plt.xlim(2000,2020)
plt.legend(loc="upper left")
plt.title("UFO Sighting By State")
plt.xlabel("Year")
<Figure size 640x480 with 1 Axes>
ufos[ufos["shape"] == "formation"].month.value_counts().sort_index().plot(label="formation")
ufos[ufos["shape"] == "fireball"].month.value_counts().sort_index().plot(label="fireball")
ufos[ufos["shape"] == "sphere"].month.value_counts().sort_index().plot(label="sphere")
ufos[ufos["shape"] == "oval"].month.value_counts().sort_index().plot(label="oval")
plt.legend()
<Figure size 640x480 with 1 Axes>
ufos["shape"].value_counts()
shape light 18230 circle 10130 triangle 7910 fireball 7095 unknown 6182 sphere 6065 other 5749 disk 4522 oval 3857 formation 2842 changing 2112 cigar 1990 flash 1675 rectangle 1597 cylinder 1374 diamond 1273 chevron 953 teardrop 767 egg 666 cone 349 cross 289 Name: count, dtype: int64
ufos[ufos["shape"] == "formation"].date_time.dt.date.value_counts().sort_index().plot(label="formation")
ufos[ufos["shape"] != "formation"].date_time.dt.date.value_counts().sort_index().plot(label="all")
<Axes: xlabel='date_time'>
<Figure size 640x480 with 1 Axes>
ufos[ufos["shape"] == "formation"].date_time.dt.date.value_counts().sort_index().plot()
# ufos[ufos["shape"] == "cigar"].date_time.dt.date.value_counts().sort_index().plot()
# ufos[ufos["shape"] == "fireball"].date_time.dt.date.value_counts().sort_index().plot()
# ufos[ufos["shape"] == "sphere"].date_time.dt.date.value_counts().sort_index().plot()
# ufos[ufos["shape"] == "sphere"].date_time.dt.date.value_counts().sort_index().plot()
ufos[ufos["shape"] != "formation"].date_time.dt.date.value_counts().sort_index().plot()
plt.xlim("2019-04-01", "2019-12-31")
(np.float64(17987.0), np.float64(18261.0))
<Figure size 640x480 with 1 Axes>
ufos
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ufos[ufos["shape"] == "circle"].date_time.dt.date.value_counts().sort_index().plot(label="circle")
ufos[ufos["shape"] == "light"].date_time.dt.date.value_counts().sort_index().plot(label="light")
ufos[ufos["shape"] == "triangle"].date_time.dt.date.value_counts().sort_index().plot(label="triangle")
ufos[ufos["shape"] == "fireball"].date_time.dt.date.value_counts().sort_index().plot(label="fireball")
ufos[ufos["shape"] == "cigar"].date_time.dt.date.value_counts().sort_index().plot(label="cigar")
plt.legend()
<Figure size 640x480 with 1 Axes>
ufos[ufos["shape"] == "circle"].year.value_counts().sort_index().plot(label="circle")
# ufos[ufos["shape"] == "light"].year.value_counts().sort_index().plot(label="light")
ufos[ufos["shape"] == "triangle"].year.value_counts().sort_index().plot(label="triangle")
ufos[ufos["shape"] == "fireball"].year.value_counts().sort_index().plot(label="fireball")
ufos[ufos["shape"] == "cigar"].year.value_counts().sort_index().plot(label="cigar")
ufos[ufos["shape"] == "formation"].year.value_counts().sort_index().plot(label="formation")
plt.legend()
plt.xlim(2010,2018)
(2010.0, 2018.0)
<Figure size 640x480 with 1 Axes>
plt.figure(figsize=(10,8))
ufos[ufos["shape"] == "circle"].year.value_counts().sort_index().plot(label="circle")
ufos[ufos["shape"] == "light"].year.value_counts().sort_index().plot(label="light")
ufos[ufos["shape"] == "triangle"].year.value_counts().sort_index().plot(label="triangle")
ufos[ufos["shape"] == "fireball"].year.value_counts().sort_index().plot(label="fireball")
ufos[ufos["shape"] == "formation"].year.value_counts().sort_index().plot(label="formation")
plt.legend()
plt.xlim(2000,2018)
plt.title("UFO Sightings By Shape (2000-2018)")
<Figure size 1000x800 with 1 Axes>
ufos[ufos["shape"] == "light"].year.value_counts().sort_index().plot(label="light", figsize=(10,8), title="UFO Sightings By Shape")
ufos[ufos["shape"] == "fireball"].year.value_counts().sort_index().plot(label="fireball")
ufos[ufos["shape"] == "circle"].year.value_counts().sort_index().plot(label="circle")
ufos[ufos["shape"] == "triangle"].year.value_counts().sort_index().plot(label="triangle")
ufos[ufos["shape"] == "formation"].year.value_counts().sort_index().plot(label="formation")

plt.xlim(2000, 2019)
plt.legend()
<Figure size 1000x800 with 1 Axes>

Subplots

df.plot(kind="hist")
<Axes: ylabel='Frequency'>
<Figure size 640x480 with 1 Axes>
df.plot(kind="hist", subplots=True, sharex=False, figsize=(5,5))
plt.tight_layout()
<Figure size 500x500 with 3 Axes>
axs = df.plot(kind="hist", subplots=True, sharex=False, layout=(1,3), figsize=(10,3), bins=30)
plt.tight_layout()
axs[0][2].set_xlim(0,20000)
axs[0][1].set_title("Middle")
axs[0][0].set_title("First")
axs[0][2].set_title("Third")
<Figure size 1000x300 with 3 Axes>
ufos.year.value_counts().sort_index().plot()
<Axes: xlabel='year'>
<Figure size 640x480 with 1 Axes>
ufos.month.value_counts().sort_index().plot(kind="bar")
<Axes: xlabel='month'>
<Figure size 640x480 with 1 Axes>
import pandas as pd
titanic = pd.read_csv("data/titanic.csv")
titanic["age"] = pd.to_numeric(titanic["age"], errors="coerce")
plt.hist(titanic["age"])
(array([ 72., 62., 274., 250., 161., 108., 65., 41., 10., 3.]), array([ 0.1667 , 8.15003, 16.13336, 24.11669, 32.10002, 40.08335, 48.06668, 56.05001, 64.03334, 72.01667, 80. ]), <BarContainer object of 10 artists>)
<Figure size 640x480 with 1 Axes>
first_class = titanic[titanic["pclass"] == 1]["age"]
second_class = titanic[titanic["pclass"] == 2]["age"]
third_class = titanic[titanic["pclass"] == 3]["age"]
plt.hist(first_class, label="1st Class", alpha=0.5, color="#f1c40f", bins=20)
plt.hist(second_class, label="2nd Class", alpha=0.5, color="#3498db", bins=20)
plt.hist(third_class, label="3rd Class", alpha=0.5, color="#e74c3c", bins=20)
plt.legend()
plt.title("Titanic Passenger Ages")
plt.xlabel("Passenger Age")
<Figure size 640x480 with 1 Axes>

The Object-Oriented Approach

fig, axs = plt.subplots(1,2)
axs[0].hist(first_class)
axs[1].hist(second_class)
axs[0].hist(third_class)
axs[0].set_title("My Title")
axs[0].set_xlabel("Age")
axs[1].set_title("My Other Title")
fig
<Figure size 640x480 with 2 Axes>
<Figure size 640x480 with 2 Axes>

Manual Subplots

fig, axs = plt.subplots(1,2)
ufos.year.value_counts().sort_index().plot(ax=axs[0])
axs[0].set_title("Sightings By Year")
ufos.month.value_counts().sort_index().plot(kind="bar", ax=axs[1])
axs[1].set_title("Sightings By Month")
<Figure size 640x480 with 2 Axes>
months = {1: 'Jan', 2: 'Feb', 3: 'Mar', 4: 'Apr', 5: 'May', 6: 'Jun', 
          7: 'Jul', 8: 'Aug', 9: 'Sep', 10: 'Oct', 11: 'Nov', 12: 'Dec'}
fig, axs = plt.subplots(2, 3, figsize=(14, 8))
fig.suptitle("UFO sightings by month", fontsize=14)
fig.tight_layout(pad=2)
ufos[ufos.year == 2014].month.value_counts().sort_index().rename(months).plot(kind="bar", ax=axs[0][0], title="2014")
ufos[ufos.year == 2015].month.value_counts().sort_index().rename(months).plot(kind="bar", ax=axs[0][1], title="2015")
ufos[ufos.year == 2016].month.value_counts().sort_index().rename(months).plot(kind="bar", ax=axs[0][2], title="2016")
ufos[ufos.year == 2017].month.value_counts().sort_index().rename(months).plot(kind="bar", ax=axs[1][0], title="2017")
ufos[ufos.year == 2018].month.value_counts().sort_index().rename(months).plot(kind="bar", ax=axs[1][1], title="2018")
ufos[ufos.year == 2019].month.value_counts().sort_index().rename(months).plot(kind="bar", ax=axs[1][2], title="2019")
plt.tight_layout()
<Figure size 1400x800 with 6 Axes>

Savefig()

ufos[ufos["shape"] == "light"].year.value_counts().sort_index().plot(label="light", figsize=(10,8), title="UFO Sightings By Shape")
ufos[ufos["shape"] == "fireball"].year.value_counts().sort_index().plot(label="fireball")
ufos[ufos["shape"] == "circle"].year.value_counts().sort_index().plot(label="circle")
ufos[ufos["shape"] == "triangle"].year.value_counts().sort_index().plot(label="triangle")
ufos[ufos["shape"] == "formation"].year.value_counts().sort_index().plot(label="formation")

plt.xlim(2000, 2019)
plt.legend()
plt.savefig("UFO_Shapes")
<Figure size 1000x800 with 1 Axes>
months = {1: 'Jan', 2: 'Feb', 3: 'Mar', 4: 'Apr', 5: 'May', 6: 'Jun', 
          7: 'Jul', 8: 'Aug', 9: 'Sep', 10: 'Oct', 11: 'Nov', 12: 'Dec'}
fig, axs = plt.subplots(2, 3, figsize=(14, 8))
fig.suptitle("UFO sightings by month", fontsize=14)
fig.tight_layout(pad=2)
ufos[ufos.year == 2014].month.value_counts().sort_index().rename(months).plot(kind="bar", ax=axs[0][0], title="2014")
ufos[ufos.year == 2015].month.value_counts().sort_index().rename(months).plot(kind="bar", ax=axs[0][1], title="2015")
ufos[ufos.year == 2016].month.value_counts().sort_index().rename(months).plot(kind="bar", ax=axs[0][2], title="2016")
ufos[ufos.year == 2017].month.value_counts().sort_index().rename(months).plot(kind="bar", ax=axs[1][0], title="2017")
ufos[ufos.year == 2018].month.value_counts().sort_index().rename(months).plot(kind="bar", ax=axs[1][1], title="2018")
ufos[ufos.year == 2019].month.value_counts().sort_index().rename(months).plot(kind="bar", ax=axs[1][2], title="2019")
plt.tight_layout()
plt.savefig("UFO_By_Month")
<Figure size 1400x800 with 6 Axes>
Data Analysis
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