A personal diary of DataFrame munging over the years.
Convert Series datatype to numeric (will error if column has non-numeric values)
(h/t @makmanalp)
pd.to_numeric(df['Column Name'])
Convert Series datatype to numeric, changing non-numeric values to NaN
(h/t @makmanalp for the updated syntax!)
pd.to_numeric(df['Column Name'], errors='coerce')
Change data type of DataFrame column
df.column_name = df.column_name.astype(np.int64)
Get a report of all duplicate records in a DataFrame, based on specific columns
dupes = df[df.duplicated(
['col1', 'col2', 'col3'], keep=False)]
List unique values in a DataFrame column
(h/t @makmanalp for the updated syntax!)
df['Column Name'].unique()
For each unique value in a DataFrame column, get a frequency count
df['Column Name'].value_counts()
Grab DataFrame rows where column = a specific value
df = df.loc[df.column == 'somevalue']
Grab DataFrame rows where column value is present in a list
test_data = {'hi': 'yo', 'bye': 'later'}
df = pd.DataFrame(list(d.items()), columns=['col1', 'col2'])
valuelist = ['yo', 'heya']
df[df.col2.isin(valuelist)]
Grab DataFrame rows where column value is not present in a list
test_data = {'hi': 'yo', 'bye': 'later'}
df = pd.DataFrame(list(d.items()), columns=['col1', 'col2'])
valuelist = ['yo', 'heya']
df[~df.col2.isin(valuelist)]
Select from DataFrame using criteria from multiple columns
(use | instead of & to do an OR)
newdf = df[(df['column_one']>2004) & (df['column_two']==9)]
Loop through rows in a DataFrame
(if you must)
for index, row in df.iterrows():
print (index, row['some column'])
Much faster way to loop through DataFrame rows if you can work with tuples
(h/t hughamacmullaniv)
for row in df.itertuples():
print(row)
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()]
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'))
Slice values in a DataFrame column (aka Series)
df.column.str[0:2]
Get quick count of rows in a DataFrame
len(df.index)
Get length of data in a DataFrame column
df.column_name.str.len()
Delete column from DataFrame
del df['column']
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)
Lower-case everything in a DataFrame column
df.column_name = df.column_name.str.lower()
Sort DataFrame by multiple columns
df = df.sort_values(
['col1','col2','col3'],ascending=[1,1,0])
Change all NaNs to None (useful before loading to a db)
df = df.where((pd.notnull(df)), None)
More pre-db insert cleanup...make a pass through the DataFrame, stripping whitespace from strings and changing any empty values to None
(not especially recommended but including here b/c I had to do this in real life once)
df = df.applymap(lambda x: str(x).strip() if len(str(x).strip()) else None)
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
(h/t: @mlevkov)
df.loc[(df['column1'] == some_value) & (df['column2'] == some_other_value), ['column_to_change']] = 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)))
Collapse hierarchical column indexes
df.columns = df.columns.get_level_values(0)
Pivot data (with flexibility about what what becomes a column and what stays a row).
pd.pivot_table(
df,values='cell_value',
index=['col1', 'col2', 'col3'], #these stay as columns; will fail silently if any of these cols have null values
columns=['col4']) #data values in this column become their own column
Concatenate two DataFrame columns into a new, single column
(useful when dealing with composite keys, for example)
(h/t @makmanalp for improving this one!)
df['newcol'] = df['col1'].astype(str) + df['col2'].astype(str)
Set up formatting so larger numbers aren't displayed in scientific notation
(h/t @thecapacity)
pd.set_option('display.float_format', lambda x: '%.3f' % x)
To display with commas and no decimals
pd.options.display.float_format = '{:,.0f}'.format
Create a DataFrame from a Python dictionary
df = pd.DataFrame(list(a_dictionary.items()), columns = ['column1', 'column2'])
Convert Django queryset to DataFrame
qs = DjangoModelName.objects.all()
q = qs.values()
df = pd.DataFrame.from_records(q)