Filtering Pandas Dataframe using OR statement

You can do like below to achieve your result:

import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
....
....
#use filter with plot
#or
fg=sns.factorplot('Retailer country', data=df1[(df1['Retailer country']=='United States') | (df1['Retailer country']=='France')], kind='count')

fg.set_xlabels('Retailer country')
plt.show()


#also
#and
fg=sns.factorplot('Retailer country', data=df1[(df1['Retailer country']=='United States') & (df1['Year']=='2013')], kind='count')

fg.set_xlabels('Retailer country')
plt.show()

From the docs:

Another common operation is the use of boolean vectors to filter the data. The operators are: | for or, & for and, and ~ for not. These must be grouped by using parentheses.

http://pandas.pydata.org/pandas-docs/version/0.15.2/indexing.html#boolean-indexing

Try:

alldata_balance = alldata[(alldata[IBRD] !=0) | (alldata[IMF] !=0)]