As you have seen, both of these processes produce similar results in the line plot. However, plt.plot is a more itemized way of creating line plots. Stylization choices are more explicit, which makes it easier to see where to change each item, but requires more coding overall. 

If ease is preferred over customization, we can choose to use a built-in style of Matplotlib. To see available styles, execute
```python
print(plt.style.available)
```
This should return a list of strings that can be passed into ```plt.style.use()``` to set a style.

Our previous line plot would look like this:
```python
from matplotlib import pyplot as plt
plt.style.use('fivethirtyeight')

x=[1,2,3,4]
y=[1,4,3,2]
y_2=[5,4,1,6]

plt.plot(x, y, label='Thing One')
plt.plot(x, y_2, label='Thing Two')

plt.xlabel('Time')
plt.ylabel('Number of things')
plt.title('Basic Plot')
plt.legend()
plt.tight_layout()

plt.savefig('plt.png')
plt.show()
```

There is a little less work to be done here for a similar result.

Pandas Dataframe vs. plt.plot

We can use Matplotlib, NumPy, and the wave module to plot waveforms of WAV files. 
```python 
import matplotlib.pyplot as plt
import numpy as np
import wave
import sys

wav=wave.open('chicken.wav', 'r')
#We only want to use mono files
if wav.getnchannels()==2:
  print('Stereo files are unsupported. Use mono files.')
  sys.exit(0) #Terminates program

raw=wav.readframes(-1) #This reads all frames, returns them in byte form
raw=np.frombuffer(raw, dtype='int16') #Converts bytes to integers
sample_rate=wav.getframerate()
time_=np.linspace(0, len(raw)/sample_rate, num=len(raw))

plt.title("Waveform of Chicken")
plt.plot(time_, raw, color="blue") #Plot time and amplitude
plt.xlabel("Time (s)")
plt.ylabel("Amplitude")
plt.tight_layout()
plt.savefig('chicken.png')
plt.show()
```
This should result in something like this:

Visualization of waveforms

Matplotlib also has a built in module that allows for visualization of line plots. 

```python
from matplotlib import pyplot as plt

x=[1,2,3,4]
y=[1,4,3,2]
y_2=[5,4,1,6]
axis=[0,1,2,3,4,5,6]
zero=[0,0,0,0,0,0,0]

plt.plot(x, y, color='m', linestyle='--', marker='.', label='Thing One')
plt.plot(x, y_2, color='b', linewidth=3, label='Thing Two')
plt.plot(axis, zero, linewidth=4, color='k')
plt.plot(zero, axis, linewidth=4,  color='k')

plt.xlabel('Time')
plt.ylabel('Number of things')
plt.title('Basic Plot')
plt.legend()
plt.tight_layout()
plt.grid(True)
plt.savefig('plt.png')
plt.show()
```



Davidson College

Using this dataframe,
```python
df = pd.DataFrame({
    'name':['john','mary','peter','jeff','bill','lisa','jose'],
    'age':[23,78,22,19,45,33,20],
    'gender':['M','F','M','M','M','F','M'],
    'state':['california','dc','california','dc','california','texas','texas'],
    'num_children':[2,0,0,3,2,1,4],
    'num_pets':[5,1,0,5,2,2,3]
})
```
To create a line plot,
```python
#gca stands for get current axis
ax = plt.gca()

#the ax parameter lets you use the same axis for multiple lines
df.plot(kind='line',x='name',y='num_children',ax=ax)
df.plot(kind='line',x='name',y='num_pets', color='red', ax=ax)

plt.show()
```

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