Plot with pandas df quickly

plot_pandas.py

# Homepage: http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.plot.html

import pandas as pd
import matplotlib.pyplot as plt


# plot
DF.plot(x='year', y=['var1', 'var2'],kind="line",subplots=False,layout=(2,2)) 
# layout: distribution of multicharts.
# subplots: plot each line in different charts.

## customize datetime xticks
xticks = pd.date_range(start=df.index.tolist()[0], end=df.index.tolist()[-1])
df.plot(ax=ax,xticks=xticks.to_pydatetime())
ax.set_xticklabels([x.strftime('%Y %b %d,%a   ') for x in xticks],rotation='vertical',fontsize='small')


# pandas plot linked to defined axes
fig, ax = plt.subplots()
DF.plot(ax=ax)


## scatter give custom points (size, color) -> 4D
df.plot(kind="scatter", x="var1", y="var2", alpha=0.4,
s=df["var3"]/100, label="var3",
c="var4", cmap=plt.get_cmap("jet"), colorbar=True,
)
plt.legend()


# SCATTER MATRIX
from pandas.tools.plotting import scatter_matrix
scatter_matrix(DF)

"""
kind : str
‘line’ : line plot (default)
‘bar’ : vertical bar plot
‘barh’ : horizontal bar plot
‘hist’ : histogram
‘box’ : boxplot
‘kde’ : Kernel Density Estimation plot
‘density’ : same as ‘kde’
‘area’ : area plot
‘pie’ : pie plot
‘scatter’ : scatter plot
‘hexbin’ : hexbin plot
"""

# display 
plt.show()