数据可视化漫谈（二）

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本文使用pandas对数据进行可视化。

---

In [1]:

%matplotlib inline

import matplotlib.pyplot as plt
import pandas as pd
import numpy as np

import seaborn as sns
sns.set()
sns.set_context('notebook', font_scale=1.5)
cp = sns.color_palette()

Thing 1: Line Chart (with many lines)

---

In [2]:

ts = pd.read_csv('data/ts.csv')

ts = ts.assign(dt = pd.to_datetime(ts.dt))
ts.head()

Out[2]:

	dt	kind	value
0	2000-01-01	A	1.442521
1	2000-01-02	A	1.981290
2	2000-01-03	A	1.586494
3	2000-01-04	A	1.378969
4	2000-01-05	A	-0.277937

In [3]:

dfp = ts.pivot(index='dt', columns='kind', values='value')
dfp.head()

Out[3]:

kind	A	B	C	D
dt
2000-01-01	1.442521	1.808741	0.437415	0.096980
2000-01-02	1.981290	2.277020	0.706127	-1.523108
2000-01-03	1.586494	3.474392	1.358063	-3.100735
2000-01-04	1.378969	2.906132	0.262223	-2.660599
2000-01-05	-0.277937	3.489553	0.796743	-3.417402

In [4]:

fig, ax = plt.subplots(1, 1, figsize=(7.5, 5))

dfp.plot(ax=ax)

ax.set(xlabel='Date',
       ylabel='Value',
       title='Random Timeseries')
    
ax.legend(loc=2)
fig.autofmt_xdate()

Thing 2: Scatter

---

In [5]:

df = pd.read_csv('data/iris.csv')
df.head()

Out[5]:

	petalLength	petalWidth	sepalLength	sepalWidth	species
0	1.4	0.2	5.1	3.5	setosa
1	1.4	0.2	4.9	3.0	setosa
2	1.3	0.2	4.7	3.2	setosa
3	1.5	0.2	4.6	3.1	setosa
4	1.4	0.2	5.0	3.6	setosa

In [6]:

fig, ax = plt.subplots(1, 1, figsize=(7.5, 7.5))

for i, s in enumerate(df.species.unique()):
    df[df.species == s].plot.scatter(
        'petalLength', 'petalWidth',
        c=cp[i], label=s, ax=ax
    )

ax.set(xlabel='Petal Length',
       ylabel='Petal Width',
       title='Petal Width v. Length -- by Species')

ax.legend(loc=2)

Out[6]:

Thing 3: Trellising the Above

---

In [7]:

dfp.plot(subplots=True, layout=(2, 2), figsize=(10, 10),
         title='Random Timeseries -- Value over Time')

Out[7]:

array([[,
        ],
       [,
        ]], dtype=object)

In [8]:

tmp_n = df.shape[0] - df.shape[0]/2

df['random_factor'] = np.random.permutation(['A'] * tmp_n + ['B'] * (df.shape[0] - tmp_n))
df.head()

Out[8]:

	petalLength	petalWidth	sepalLength	sepalWidth	species	random_factor
0	1.4	0.2	5.1	3.5	setosa	A
1	1.4	0.2	4.9	3.0	setosa	A
2	1.3	0.2	4.7	3.2	setosa	B
3	1.5	0.2	4.6	3.1	setosa	B
4	1.4	0.2	5.0	3.6	setosa	B

Thing 4: Visualizing Distributions (Boxplot and Histogram)

---

In [9]:

fig, ax = plt.subplots(1, 1, figsize=(10, 10))

df.boxplot(column='petalWidth', by='species', ax=ax)

Out[9]:

In [10]:

sns.set_context('notebook', font_scale=1.25)

fig, ax = plt.subplots(1, 1, figsize=(10, 10))

df.boxplot(column='petalWidth', by=['species', 'random_factor'], ax=ax)

Out[10]:

In [11]:

sns.set_context('notebook', font_scale=1.5)

fig, ax = plt.subplots(1, 1, figsize=(10, 10))

df.hist(column='petalWidth', by='species', grid=None, ax=ax)

/Applications/anaconda/lib/python2.7/site-packages/pandas/tools/plotting.py:3369: UserWarning: To output multiple subplots, the figure containing the passed axes is being cleared
  "the passed axes is being cleared", UserWarning)

Out[11]:

array([[,
        ],
       [,
        ]], dtype=object)

Thing 5: Bar Chart

---

In [12]:

df = pd.read_csv('data/titanic.csv')
df.head()

Out[12]:

	survived	pclass	sex	age	sibsp	parch	fare	embarked	class	who	adult_male	deck	embark_town	alive	alone
0	0	3	male	22.0	1	0	7.2500	S	Third	man	True	NaN	Southampton	no	False
1	1	1	female	38.0	1	0	71.2833	C	First	woman	False	C	Cherbourg	yes	False
2	1	3	female	26.0	0	0	7.9250	S	Third	woman	False	NaN	Southampton	yes	True
3	1	1	female	35.0	1	0	53.1000	S	First	woman	False	C	Southampton	yes	False
4	0	3	male	35.0	0	0	8.0500	S	Third	man	True	NaN	Southampton	no	True

In [13]:

dfg = df.groupby(['survived', 'pclass']).agg({'fare': 'mean'})
dfg

Out[13]:

		fare
survived	pclass
0	1	64.684008
	2	19.412328
	3	13.669364
1	1	95.608029
	2	22.055700
	3	13.694887

In [14]:

fig, ax = plt.subplots(1, 1, figsize=(12.5, 7))

dfg.reset_index().\
    pivot(index='pclass',
          columns='survived',
          values='fare').plot.bar(ax=ax)

ax.set(xlabel='Class',
       ylabel='Fare',
       title='Fare by survival and class')

Out[14]:

[,
 ,
 ]