Pandas Data Visualization¶

Feng Li

School of Statistics and Mathematics

Central University of Finance and Economics

feng.li@cufe.edu.cn

https://feng.li/python

Plot within Pandas¶

Pandas provide the basics in pandas to easily create decent looking plots. This section demonstrates visualization through charting. Pandas uses the standard convention for referencing the matplotlib API. You have to install matplotlib first before you use:

pip3 install matplotlib --user
  • Pandas plot
In [1]:
import matplotlib.pyplot as plt
import pandas as pd

Basic plotting: plot¶

  • The plot method on Series and DataFrame is just a simple wrapper around plt.plot():
In [2]:
import numpy as np
ts = pd.Series(np.random.randn(1000), index=pd.date_range("1/1/2000", periods=1000))
ts
Out[2]:
2000-01-01   -1.473910
2000-01-02   -0.947044
2000-01-03    0.354257
2000-01-04    1.081735
2000-01-05   -0.121218
                ...   
2002-09-22   -1.395644
2002-09-23   -1.096312
2002-09-24   -0.814845
2002-09-25    1.115848
2002-09-26   -0.004515
Freq: D, Length: 1000, dtype: float64
In [3]:
ts.plot()
Out[3]:
<AxesSubplot:>
In [4]:
ts2 = ts.cumsum()
ts2.plot()
Out[4]:
<AxesSubplot:>
  • On DataFrame, plot() is a convenience to plot all of the columns with labels:
In [5]:
df = pd.DataFrame(np.random.randn(1000, 4), index=ts.index, columns=list("ABCD"))
df
Out[5]:
A B C D
2000-01-01 -0.669035 -1.360874 -1.019936 0.979693
2000-01-02 1.677242 -0.649723 -0.208590 -0.628792
2000-01-03 -0.734900 1.171610 1.795734 -0.228767
2000-01-04 1.892811 -0.163232 0.998399 0.978396
2000-01-05 -0.701403 -0.089810 1.914202 0.401459
... ... ... ... ...
2002-09-22 1.193628 0.570094 0.481622 -1.106532
2002-09-23 0.749351 0.565093 -2.290245 -1.491666
2002-09-24 0.082742 2.089998 1.298623 0.990459
2002-09-25 -3.236171 -0.101329 -2.281272 -0.963969
2002-09-26 -0.243259 0.537689 0.979776 -0.630714

1000 rows × 4 columns

In [6]:
df = df.cumsum()
df.plot()
Out[6]:
<AxesSubplot:>
  • To plot data on a secondary y-axis, use the secondary_y keyword:
In [7]:
plt.figure()
ax = df.plot(secondary_y=["A", "B"])
ax.set_ylabel("C, D scale")
ax.right_ax.set_ylabel("A, B scale")

<Figure size 432x288 with 0 Axes>
In [8]:
df.plot(subplots=True, figsize=(6, 6));
In [9]:
df.plot(subplots=True, layout=(2, 2), figsize=(6, 6), sharex=True);
  • You can plot one column versus another using the x and y keywords in plot():
In [10]:
df.plot(x="A", y="B", kind="scatter")

  • Plotting methods allow for a handful of plot styles other than the default line plot. These methods can be provided as the kind keyword argument to plot(), and include:

    • bar or barh for bar plots

    • hist for histogram

    • box for boxplot

    • kde or density for density plots

    • area for area plots

    • scatter for scatter plots

    • hexbin for hexagonal bin plots

    • pie for pie plots

In [11]:
plt.figure()
df.iloc[5].plot.bar()
plt.axhline(0, color="k")
Out[11]:
<matplotlib.lines.Line2D at 0x7f37f583a340>
In [12]:
df2 = pd.DataFrame(np.random.rand(10, 4), columns=["a", "b", "c", "d"])
df2.plot.bar()
In [13]:
df2.plot.bar(stacked=True);
In [14]:
df2.plot.barh(stacked=True);
  • Boxplot can be drawn calling Series.plot.box() and DataFrame.plot.box(), or DataFrame.boxplot() to visualize the distribution of values within each column.
In [15]:
df4 = pd.DataFrame(np.random.rand(10, 5), columns=["A", "B", "C", "D", "E"])

df4.plot.box();

Area plot¶

  • You can create area plots with Series.plot.area() and DataFrame.plot.area(). Area plots are stacked by default. To produce stacked area plot, each column must be either all positive or all negative values.
In [16]:
df5 = pd.DataFrame(np.random.rand(10, 4), columns=["a", "b", "c", "d"])

df5.plot.area();
  • To produce an unstacked plot, pass stacked=False. Alpha value is set to 0.5 unless otherwise specified:
In [17]:
df5.plot.area(stacked=False);

Scatter plot¶

  • Scatter plot can be drawn by using the DataFrame.plot.scatter() method. Scatter plot requires numeric columns for the x and y axes. These can be specified by the x and y keywords.
In [18]:
import numpy as np
df = pd.DataFrame(np.random.rand(50, 4), columns=["a", "b", "c", "d"])
df["species"] = pd.Categorical(
    ["setosa"] * 20 + ["versicolor"] * 20 + ["virginica"] * 10)
df
Out[18]:
a b c d species
0 0.829634 0.736783 0.261702 0.193386 setosa
1 0.911153 0.419372 0.764008 0.829193 setosa
2 0.952910 0.989072 0.135449 0.726242 setosa
3 0.835357 0.505718 0.695249 0.658092 setosa
4 0.273602 0.894777 0.167112 0.849104 setosa
5 0.112161 0.321692 0.525190 0.504617 setosa
6 0.485931 0.672482 0.508185 0.446647 setosa
7 0.961216 0.929337 0.575983 0.518848 setosa
8 0.388551 0.303547 0.519219 0.191637 setosa
9 0.921188 0.935272 0.314077 0.639557 setosa
10 0.940223 0.201431 0.329552 0.686811 setosa
11 0.291310 0.233028 0.595589 0.908610 setosa
12 0.094035 0.602216 0.614800 0.995167 setosa
13 0.227287 0.766969 0.511284 0.389168 setosa
14 0.552372 0.864271 0.560369 0.991117 setosa
15 0.641082 0.942939 0.579271 0.120085 setosa
16 0.179040 0.738828 0.691198 0.002092 setosa
17 0.361708 0.152825 0.954904 0.114083 setosa
18 0.884529 0.820541 0.099396 0.233853 setosa
19 0.447590 0.501434 0.852245 0.819603 setosa
20 0.174967 0.719492 0.557812 0.044909 versicolor
21 0.236692 0.923369 0.119679 0.073185 versicolor
22 0.038410 0.897120 0.334372 0.724111 versicolor
23 0.287369 0.339598 0.403237 0.230662 versicolor
24 0.016391 0.416675 0.382465 0.504789 versicolor
25 0.707238 0.278726 0.568196 0.291667 versicolor
26 0.446536 0.672371 0.997601 0.723074 versicolor
27 0.177259 0.527396 0.954444 0.308312 versicolor
28 0.385002 0.251081 0.142673 0.097961 versicolor
29 0.414637 0.512343 0.214940 0.303209 versicolor
30 0.461065 0.310299 0.812599 0.222754 versicolor
31 0.679068 0.486953 0.997664 0.698419 versicolor
32 0.014557 0.005532 0.892814 0.001630 versicolor
33 0.510225 0.474914 0.404952 0.599638 versicolor
34 0.666406 0.402967 0.895481 0.871515 versicolor
35 0.457762 0.535438 0.049200 0.933645 versicolor
36 0.741729 0.774966 0.306492 0.918807 versicolor
37 0.055785 0.269289 0.141268 0.256702 versicolor
38 0.906972 0.168197 0.110812 0.336639 versicolor
39 0.060806 0.542802 0.196161 0.132549 versicolor
40 0.198089 0.164904 0.291279 0.038471 virginica
41 0.308591 0.714715 0.202786 0.432081 virginica
42 0.700326 0.756655 0.085023 0.363134 virginica
43 0.133076 0.392832 0.683437 0.831690 virginica
44 0.842299 0.319654 0.970484 0.191483 virginica
45 0.332679 0.630887 0.532070 0.871402 virginica
46 0.439732 0.373100 0.030907 0.807853 virginica
47 0.393857 0.767928 0.148180 0.974557 virginica
48 0.756885 0.547357 0.820595 0.905695 virginica
49 0.736046 0.061893 0.028358 0.923899 virginica
In [19]:
df.plot.scatter(x="a", y="b");
In [20]:
# The keyword c may be given as the name of a column to provide colors for each point:
df.plot.scatter(x="a", y="b", c="species", cmap="viridis", s=50);

Hexagonal bin plot¶

  • You can create hexagonal bin plots with DataFrame.plot.hexbin(). Hexbin plots can be a useful alternative to scatter plots if your data are too dense to plot each point individually.
In [21]:
df = pd.DataFrame(np.random.randn(10000, 2), columns=["a", "b"])
df["b"] = df["b"] + np.arange(10000)
In [22]:
df.plot.scatter(x="a", y="b") # bad scatter plot without hexbin
Out[22]:
<AxesSubplot:xlabel='a', ylabel='b'>