bsweger/useful_pandas_snippets.md

## useful_pandas_snippets.md

      
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              useful_pandas_snippets.md
            
          
    Useful Pandas Snippets

A personal diary of DataFrame munging over the years.
Data Types and Conversion

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)

Exploring and Finding Data

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()

Updating and Cleaning Data

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)

Reshaping, Concatenating, and Merging Data

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)

Display and formatting

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

Creating DataFrames

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)

Creating Series

Create a new series using an index from an existing series
# original_series = the original series
# simplisitically generate a list of values to use in the new series
new_series_values = list(range(1, len(original_series) + 1))
new_series = pd.Series(new_series_values, index=original_series.index)