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#List unique values in a DataFrame column
pd.unique(df.column_name.ravel())
#Convert Series datatype to numeric, getting rid of any non-numeric values
df['col'] = df['col'].astype(str).convert_objects(convert_numeric=True)
#Grab DataFrame rows where column has certain values
valuelist = ['value1', 'value2', 'value3']
df = df[df.column.isin(value_list)]
#Grab DataFrame rows where column doesn't have certain values
valuelist = ['value1', 'value2', 'value3']
df = df[~df.column.isin(value_list)]
#Delete column from DataFrame
del df['column']
#Select from DataFrame using criteria from multiple columns
newdf = df[(df['column_one']>2004) & (df['column_two']==9)]
#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',
})
#lower-case all DataFrame column names
df.columns = map(str.lower, df.columns)
#even more fancy DataFrame column re-naming
#lower-case all DataFrame column names (for example)
df.rename(columns=lambda x: x.split('.')[-1], inplace=True)
#Loop through rows in a DataFrame
#(if you must)
for index, row in df:
print index, row['some column']
#Lower-case everything in a DataFrame column
df.column_name = df.column_name.str.lower()
#Sort dataframe by multiple columns
df = df.sort(['col1','col2','col3'],ascending=[1,1,0])
#get top n for each group of columns in a sorted dataframe
#(make sure dataframe is sorted first)
top5 = df.groupby(['groupingcol1', 'groupingcol2']).head(5)
#Grab DataFrame rows where specific column is null/notnull
newdf = df[df['column'].isnull()]
len(newdf)
#select from DataFrame using multiple keys of a hierarchical index
df.xs(('index level 1 value','index level 2 value'), level=('level 1','level 2'))
#Change all NaNs to None (useful before
#loading to a db)
df = df.where((pd.notnull(df)), None)
#Slice values in a DataFrame column (aka Series)
df.column.str[0:2]
#Get quick count of rows in a DataFrame
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
#change data type of DataFrame column
df.column_name = df.column_name.astype(np.int64)
# Get rid of non-numeric values throughout a DataFrame:
for col in refunds.columns.values:
refunds[col] = refunds[col].replace('[^0-9]+.-', '', regex=True)
#Set DataFrame column values based on other column values
df['column_to_change'][(df['column1'] == some_value) & (df['column2'] == some_other_value)] = new_value
#Clean up missing values in multiple DataFrame columns
df = df.fillna({
'col1': 'missing',
'col2': '99.999',
'col3': '999',
'col4': 'missing',
'col5': 'missing',
'col6': '99'
})
#Doing calculations with DataFrame columns that have missing values
#In example below, swap in 0 for df['col1'] cells that contain null
df['new_col'] = np.where(pd.isnull(df['col1']),0,df['col1']) + df['col2']
# 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 Django queryset to DataFrame
#convert queryset to dataframe
qs = DjangoModelName.objects.all()
q = qs.values()
df = pd.DataFrame.from_records(q)
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