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Plotting Series and DataFrame objects¶
- Plotting the data of a Series or DataFrame object can be accomplished by using the matplotlib.pyplot methods and functions.
- However, as of version 0.17.0 pandas objects Series and DataFrame come equipped with their own .plot() methods. Thus, if you have a Series or DataFrame type object (let's say 's' or 'df') you can call the plot method by writing:
s.plot() or df.plot() - Keep in mind that in order to be flexible, the plot() method accepts a considerable number of arguments that can only be learned by practicing various plotting scenarios.
- The examples below demonstrate common scenarios for plotting data in Series and DataFrame objects.
1. Plot Series object¶
- Most often we would like to plot a Series obect using the index labels as axis-x and the data values as axis-y. This can be easily accomplished with the matplotlib.pyplot plot() method
In [2]:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
s = pd.Series([np.random.randint(1,100) for i in range(1,100)])
p = plt.plot(s.index, s.values)
In [18]:
s = pd.Series(np.sin(np.linspace(0, 2*np.pi, 100)), index=np.linspace(0, 2*np.pi, 100))
plt.xticks(np.linspace(0, 2*np.pi, 5),('0', 'Ï€/2', 'Ï€', '3Ï€/2', '2Ï€'))
p = plt.plot(s.index, s.values)
In [93]:
s = pd.Series([np.random.randint(1,100) for i in range(1,100)])
n, bins, patches = plt.hist(s.values, color='cyan', alpha=0.5)
- Alternatively you can use the Series.plot() method
In [97]:
s = pd.Series([np.random.randint(1,100) for i in range(1,100)])
p = s.plot(kind='hist', color='r', alpha=0.5)
See the Series.plot() documentation for explanations on the arguments that the method accepts.¶
2. Plot DataFrame object¶
First make sure that you run the code below so that df is properly constructed to further run the examples
In [22]:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
df = pd.read_csv("../../data/sampledata.csv",sep=',',skiprows=3, header=0, index_col=0)
#delete column 'Country' to only have columns with float data
del df['Country']
df = df/1e6
df
Out[22]:
Extract and plot one Column data¶
- Use .xs() DataFrame method (and pass the name of the column as argument) to get the column as a Series object. Remember to also set the 'axis=1' argument to denote the column direction.
In [25]:
s = df.xs('2015', axis=1)
s
Out[25]:
- Call Series.plot() to plot the Series object
In [26]:
# plotting a barchart
p1 = s.plot(kind='bar', title='Countries population in millions in 2005', yticks=[10, 40, 80])
Extract and plot one Row data¶
- Use .xs() again (but with 'axis=0' or no axis at all: defaults to 0) and pass the index row as argument to get the row as a Series object.
In [27]:
s = df.xs('ESP')
p = s.plot()
In [28]:
p1 = s.plot(kind='bar', ylim=(44,47))
p2 = s.plot(kind='line', colormap='Reds_r', title='Population of Spain')
Plotting DataFrame Column vs. Column¶
In [52]:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
data = {'country': ['Italy','Spain','Greece','France','Portugal'],
'popu': [61, 46, 11, 65, 10],
'percent': [0.83,0.63,0.15,0.88,0.14],
'area': [301,506,132,641,92]}
df = pd.DataFrame(data, index=['ITA', 'ESP', 'GRC', 'FRA', 'PRT'])
df.sort_values('country', axis=0, ascending=True, inplace=True)
df
Out[52]:
In [63]:
df.plot('area', 'popu', kind='scatter')
df.plot(df.index, 'area', kind='bar')
Out[63]:
In [72]:
# multiple plots
fig, axes = plt.subplots(nrows=2, ncols=1)
p1 = df['popu'].plot(kind='bar', ax=axes[0], figsize=(10, 10), title='Population', alpha=0.5)
p2 = df['area'].plot(kind='bar', ax=axes[1], figsize=(10, 10), title='Area', color='r', alpha=0.5)
See the DataFrame.plot() documentation for explanations on the arguments that the method accepts.¶
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