Kembali ke EDA
Matplotlib adalah library yang digunakan untuk visualisasi data. Hasil visualisasi data matplotllib menyerupai hasil visualisasi pada bahasa pemrograman matlab. Library ini bukanlah cara yang paling mudah untuk menghasilkan visualisasi data, tetapi visualisasi yang dihasilkan fleksibel dan dapat digunakan untuk banyak sekali kasus.
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
# Import Dataset
df = pd.read_csv('https://raw.githubusercontent.com/farhanage/dataset-for-study/main/insurance.csv')
# Cek 5 observasi pertama
df.head()| age | sex | bmi | children | smoker | region | charges | |
|---|---|---|---|---|---|---|---|
| 0 | 19 | female | 27.900 | 0 | yes | southwest | 16884.92400 |
| 1 | 18 | male | 33.770 | 1 | no | southeast | 1725.55230 |
| 2 | 28 | male | 33.000 | 3 | no | southeast | 4449.46200 |
| 3 | 33 | male | 22.705 | 0 | no | northwest | 21984.47061 |
| 4 | 32 | male | 28.880 | 0 | no | northwest | 3866.85520 |
# melihat informasi mengenai tiap variabel
df.info()<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1338 entries, 0 to 1337
Data columns (total 7 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 age 1338 non-null int64
1 sex 1338 non-null object
2 bmi 1338 non-null float64
3 children 1338 non-null int64
4 smoker 1338 non-null object
5 region 1338 non-null object
6 charges 1338 non-null float64
dtypes: float64(2), int64(2), object(3)
memory usage: 73.3+ KB# statistik deskritif semua variabel numerik
df.describe()| age | bmi | children | charges | |
|---|---|---|---|---|
| count | 1338.000000 | 1338.000000 | 1338.000000 | 1338.000000 |
| mean | 39.207025 | 30.663397 | 1.094918 | 13270.422265 |
| std | 14.049960 | 6.098187 | 1.205493 | 12110.011237 |
| min | 18.000000 | 15.960000 | 0.000000 | 1121.873900 |
| 25% | 27.000000 | 26.296250 | 0.000000 | 4740.287150 |
| 50% | 39.000000 | 30.400000 | 1.000000 | 9382.033000 |
| 75% | 51.000000 | 34.693750 | 2.000000 | 16639.912515 |
| max | 64.000000 | 53.130000 | 5.000000 | 63770.428010 |
# Box Plot variabel `age`
plt.boxplot(x='age', data=df)
# Menambahkan Judul Plot
plt.title("Box Plot")
# Menambahkan label sumbu X dan Y
plt.xlabel('Age')
plt.ylabel('Value')
# Menunjukkan plot
plt.show()
# Box Plot variabel `bmi`
plt.boxplot(x='bmi', data=df)
# Menambahkan Judul Plot
plt.title("Box Plot")
# Menambahkan label sumbu X dan Y
plt.xlabel('bmi')
plt.ylabel('Value')
# Menunjukkan plot
plt.show()
Seperti halnya penggunaan syntax ? pada bahasa pemrograman R, kita dapat mengakses dokumentasi suatu fungsi dalam suatu modul pada python dengan menggunakan function help()
# Melihat dokumentasi mengenai function plt.boxplot()
help(plt.boxplot)Help on function boxplot in module matplotlib.pyplot:
boxplot(x: 'ArrayLike | Sequence[ArrayLike]', notch: 'bool | None' = None, sym: 'str | None' = None, vert: 'bool | None' = None, whis: 'float | tuple[float, float] | None' = None, positions: 'ArrayLike | None' = None, widths: 'float | ArrayLike | None' = None, patch_artist: 'bool | None' = None, bootstrap: 'int | None' = None, usermedians: 'ArrayLike | None' = None, conf_intervals: 'ArrayLike | None' = None, meanline: 'bool | None' = None, showmeans: 'bool | None' = None, showcaps: 'bool | None' = None, showbox: 'bool | None' = None, showfliers: 'bool | None' = None, boxprops: 'dict[str, Any] | None' = None, labels: 'Sequence[str] | None' = None, flierprops: 'dict[str, Any] | None' = None, medianprops: 'dict[str, Any] | None' = None, meanprops: 'dict[str, Any] | None' = None, capprops: 'dict[str, Any] | None' = None, whiskerprops: 'dict[str, Any] | None' = None, manage_ticks: 'bool' = True, autorange: 'bool' = False, zorder: 'float | None' = None, capwidths: 'float | ArrayLike | None' = None, *, data=None) -> 'dict[str, Any]'
Draw a box and whisker plot.
The box extends from the first quartile (Q1) to the third
quartile (Q3) of the data, with a line at the median.
The whiskers extend from the box to the farthest data point
lying within 1.5x the inter-quartile range (IQR) from the box.
Flier points are those past the end of the whiskers.
See https://en.wikipedia.org/wiki/Box_plot for reference.
.. code-block:: none
Q1-1.5IQR Q1 median Q3 Q3+1.5IQR
|-----:-----|
o |--------| : |--------| o o
|-----:-----|
flier <-----------> fliers
IQR
Parameters
----------
x : Array or a sequence of vectors.
The input data. If a 2D array, a boxplot is drawn for each column
in *x*. If a sequence of 1D arrays, a boxplot is drawn for each
array in *x*.
notch : bool, default: False
Whether to draw a notched boxplot (`True`), or a rectangular
boxplot (`False`). The notches represent the confidence interval
(CI) around the median. The documentation for *bootstrap*
describes how the locations of the notches are computed by
default, but their locations may also be overridden by setting the
*conf_intervals* parameter.
.. note::
In cases where the values of the CI are less than the
lower quartile or greater than the upper quartile, the
notches will extend beyond the box, giving it a
distinctive "flipped" appearance. This is expected
behavior and consistent with other statistical
visualization packages.
sym : str, optional
The default symbol for flier points. An empty string ('') hides
the fliers. If `None`, then the fliers default to 'b+'. More
control is provided by the *flierprops* parameter.
vert : bool, default: True
If `True`, draws vertical boxes.
If `False`, draw horizontal boxes.
whis : float or (float, float), default: 1.5
The position of the whiskers.
If a float, the lower whisker is at the lowest datum above
``Q1 - whis*(Q3-Q1)``, and the upper whisker at the highest datum
below ``Q3 + whis*(Q3-Q1)``, where Q1 and Q3 are the first and
third quartiles. The default value of ``whis = 1.5`` corresponds
to Tukey's original definition of boxplots.
If a pair of floats, they indicate the percentiles at which to
draw the whiskers (e.g., (5, 95)). In particular, setting this to
(0, 100) results in whiskers covering the whole range of the data.
In the edge case where ``Q1 == Q3``, *whis* is automatically set
to (0, 100) (cover the whole range of the data) if *autorange* is
True.
Beyond the whiskers, data are considered outliers and are plotted
as individual points.
bootstrap : int, optional
Specifies whether to bootstrap the confidence intervals
around the median for notched boxplots. If *bootstrap* is
None, no bootstrapping is performed, and notches are
calculated using a Gaussian-based asymptotic approximation
(see McGill, R., Tukey, J.W., and Larsen, W.A., 1978, and
Kendall and Stuart, 1967). Otherwise, bootstrap specifies
the number of times to bootstrap the median to determine its
95% confidence intervals. Values between 1000 and 10000 are
recommended.
usermedians : 1D array-like, optional
A 1D array-like of length ``len(x)``. Each entry that is not
`None` forces the value of the median for the corresponding
dataset. For entries that are `None`, the medians are computed
by Matplotlib as normal.
conf_intervals : array-like, optional
A 2D array-like of shape ``(len(x), 2)``. Each entry that is not
None forces the location of the corresponding notch (which is
only drawn if *notch* is `True`). For entries that are `None`,
the notches are computed by the method specified by the other
parameters (e.g., *bootstrap*).
positions : array-like, optional
The positions of the boxes. The ticks and limits are
automatically set to match the positions. Defaults to
``range(1, N+1)`` where N is the number of boxes to be drawn.
widths : float or array-like
The widths of the boxes. The default is 0.5, or ``0.15*(distance
between extreme positions)``, if that is smaller.
patch_artist : bool, default: False
If `False` produces boxes with the Line2D artist. Otherwise,
boxes are drawn with Patch artists.
labels : sequence, optional
Labels for each dataset (one per dataset).
manage_ticks : bool, default: True
If True, the tick locations and labels will be adjusted to match
the boxplot positions.
autorange : bool, default: False
When `True` and the data are distributed such that the 25th and
75th percentiles are equal, *whis* is set to (0, 100) such
that the whisker ends are at the minimum and maximum of the data.
meanline : bool, default: False
If `True` (and *showmeans* is `True`), will try to render the
mean as a line spanning the full width of the box according to
*meanprops* (see below). Not recommended if *shownotches* is also
True. Otherwise, means will be shown as points.
zorder : float, default: ``Line2D.zorder = 2``
The zorder of the boxplot.
Returns
-------
dict
A dictionary mapping each component of the boxplot to a list
of the `.Line2D` instances created. That dictionary has the
following keys (assuming vertical boxplots):
- ``boxes``: the main body of the boxplot showing the
quartiles and the median's confidence intervals if
enabled.
- ``medians``: horizontal lines at the median of each box.
- ``whiskers``: the vertical lines extending to the most
extreme, non-outlier data points.
- ``caps``: the horizontal lines at the ends of the
whiskers.
- ``fliers``: points representing data that extend beyond
the whiskers (fliers).
- ``means``: points or lines representing the means.
Other Parameters
----------------
showcaps : bool, default: True
Show the caps on the ends of whiskers.
showbox : bool, default: True
Show the central box.
showfliers : bool, default: True
Show the outliers beyond the caps.
showmeans : bool, default: False
Show the arithmetic means.
capprops : dict, default: None
The style of the caps.
capwidths : float or array, default: None
The widths of the caps.
boxprops : dict, default: None
The style of the box.
whiskerprops : dict, default: None
The style of the whiskers.
flierprops : dict, default: None
The style of the fliers.
medianprops : dict, default: None
The style of the median.
meanprops : dict, default: None
The style of the mean.
data : indexable object, optional
If given, all parameters also accept a string ``s``, which is
interpreted as ``data[s]`` (unless this raises an exception).
See Also
--------
violinplot : Draw an estimate of the probability density function.
# Histogram variabel `bmi`
plt.hist(x='bmi', data=df)
# Menambahkan Judul Plot
plt.title("Histogram")
# Menambahkan label sumbu X dan Y
plt.xlabel('bmi')
plt.ylabel('Count')
# Menunjukkan plot
plt.show()
# Hitung banyaknya responden dari masing-masing gender
df['sex'].value_counts()sex
male 676
female 662
Name: count, dtype: int64# Bar chart jumlah tiap jenis kelamin
df['sex'].value_counts().plot(kind='bar')
# Menambahkan Judul Plot
plt.title("Bar Chart")
# Menambahkan label sumbu X dan Y
plt.xlabel('sex')
plt.ylabel('count')
# Menunjukkan plot
plt.show()
# Horizontal Bar chart jumlah tiap jenis kelamin
df['sex'].value_counts().plot(kind='barh')
# Menambahkan Judul Plot
plt.title("Bar Chart")
# Menambahkan label sumbu X dan Y
plt.xlabel('count')
plt.ylabel('sex')
# Menunjukkan plot
plt.show()
# Pie chart persentase sebaran region seluruh responden
df['region'].value_counts().plot(kind='pie', autopct='%1.1f%%')
# Menambahkan Judul Plot
plt.title("Pie Chart")
# Menunjukkan plot
plt.show()
# Scatter plot variabel `age` dan `charges`
df.plot(kind='scatter', x='age', y='charges')
# Menambahkan Judul Plot
plt.title("Scatter Plot `Age` vs `Charges`")
# Menambahkan label sumbu X dan Y
plt.xlabel('Age')
plt.ylabel('Charges')
# Menunjukkan plot
plt.show()
Untuk plot lainnya, silakan telusuri dokumentasi dari library matplotlib yang dapat diakses pada link berikut : Dokumentasi matplotlib.pyplot
Pembuatan suatu plot menggunakan library matplotlib akan menghasilkan suatu figure yang memiliki beberapa komponen di dalamnya.
Berikut cara membuat suatu figure menggunakan matplotlib
fig = plt.figure() # an empty figure with no Axes
plt.show()<Figure size 672x480 with 0 Axes>Figure kosong tidak dapat divisualisasikan. Untuk membuat suatu figure yang memiliki axes, gunakan function plt.subplots()
fig, ax = plt.subplots() # a figure with a single Axes
plt.show()
Bagaimana jika kita ingin membuat gabungan dari beberapa Axes dalam 1 figure?
function subplots menerima parameter jumlah baris dan jumlah kolom untuk membentuk suatu grid yang terdiri atas 1 atau lebih axes
fig, axs = plt.subplots(2, 2) # a figure with a 2x2 grid of Axes
plt.show()
Selain dengan function subplots, ada juga function subplot_mosaic yang akan menghasilkan axes dengan ukuran yang lebih bervariasi.
# a figure with one axes on the left, and two on the right:
fig, axs = plt.subplot_mosaic([['left', 'right_top'],
['left', 'right_bottom']])
plt.show()
Untuk menambahkan plot pada tiap axis, gunakan function-function plot pada axis dengan index yang bersesuaian.
fig, axs = plt.subplots(2, 2, layout="constrained")
axs[0,0].hist(df['age'])
axs[0,0].set_title('Variabel `Age`')
axs[0,1].hist(df['bmi'])
axs[0,1].set_title('Variabel `bmi`')
axs[1,0].hist(df['children'])
axs[1,0].set_title('Variabel `children`')
axs[1,1].hist(df['charges'])
axs[1,1].set_title('Variabel `charges`')
fig.suptitle('Histogram Variabel Numerik')
plt.show()
Lebih lanjut, silakan baca dokumentasi dari plt.subplot pada link berikut : Dokumentasi plt.subplots