# Get rid of non-numeric values throughout a DataFrame:
for col in refunds.columns.values:
  refunds[col] = refunds[col].replace('[^0-9]+.-', '', regex=True)
  
# Split delimited values in a DataFrame column into two new columns
df['new_col1'], df['new_col2'] = zip(*df['original_col'].apply(lambda x: x.split(': ', 1)))

#Convert Series datatype to numeric, getting rid of any values that are non-numeric
df['col'] = df['col'].astype(str).convert_objects(convert_numeric=True)

#Clean up missing values in multiple DataFrame columns
df = df.fillna({
    'fyq': 'missing',
    'cfda_program_num': '99.999',
    'recipient_county_code': '999',
    'recipient_country_code': 'missing',
    'uri': 'missing',
    'recipient_state_code': '99'
})

#Grab DataFrame rows where specified column has certain values
valuelist = ['value1', 'value2', 'value3']
df = df[df.column.isin(value_list)]

#Grab DataFrame rows where specified column doesn't have certain values
valuelist = ['value1', 'value2', 'value3']
df = df[~df.column.isin(value_list)]

#Rename several DataFrame columns
df = df.rename(columns = {
    'col1 old name':'col1 new name',
    'col2 old name':'col2 new name',
    'col3 old name':'col3 new name',
})

#Convert Django queryset to DataFrame
#convert queryset to dataframe
qs = DjangoModelName.objects.all()
q = qs.values()
df = pd.DataFrame.from_records(q)

#Loop through rows in a DataFrame
for index, row in df:
    print index, row['some column']  

#lower-case all DataFrame column names
df.columns = map(str.lower, df.columns)

#Lower-case everything in a DataFrame column
df.column_name = df.column_name.str.lower()

#Grab DataFrame rows where specific column is null/notnull
newdf = df[df['column'].isnull()]
len(newdf)

#Change all NaNs to None (useful before
#loading to a db)
df = df.where((pd.notnull(df)), None)

#List unique values in a DataFrame column
pd.unique(df.column_name.ravel())

#Select from DataFrame using criteria from multiple columns
newdf = df[(df['column_one']>2004) & (df['column_two']==9)]

#Get quick count of rows in a DataFrame
#(note that using df[0].count will exclude NaNs)
len(df.index)

#Pivot data (with flexibility about what what
#becomes a column and what stays a row).
#Syntax works on Pandas >= .14
pd.pivot_table(
  df,values='cell_value',
  index=['col1', 'col2', 'col3'], #these stay as columns
  columns=['col4']) #data values in this column become their own column