Pandas, Numpy

Pandas, Numpy - quick access

#If this is set to True, the axes which are reduced are left in the result as dimensions with size one
<np array>.sum(axis=1, keepdims=True)
np.vstack - stack array row-wise 

#Convert to NP Array:
df = pd.read_csv(csvfile)
nparr = np.array(df)

#finding max of a dataframe
pd.DataFrame.max(df)

#selection on rows and columns:
df.loc[:3,['2003','2005']]

#index
df.set_index('Country', inplace=True)

#select row based on row name:
df.loc['rowname']
df.loc[['Assam','Karnataka'],['2003','2004']]  #i.e. [[rownames],[colnames]]

#select row based on a value in the column:
df.loc[df['column_name'] == some_value_in_the_table

#fill NaN
df = df.fillna(0)

#get max of all values in the dataframe:
df.values.max()