Useful Pandas Snippets

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)

Immensely helpful. Kudos for preparing this 👍

refunds[col].replace('[^0-9]+.-', '', regex=True) was a great find for me...thank you!

This is huge!

Fantastic!

Thanks for sharing! Very helpful!

Thanks for this!! really helpful and useful!

# Validate datetime
day,month,year = inputDate.split('/')
isValidDate = True
try :
    datetime.datetime(int(year),int(month),int(day))
except ValueError :
    isValidDate = False
if(isValidDate) :
    print ("Input date is valid ..")
else :
    print ("Input date is not valid..")

This is extremly useful. Thank you

Typo: Should be df.columns (df.columnS) in https://gist.github.com/bsweger/e5817488d161f37dcbd2#file-useful_pandas_snippets-py-L13-L19

Actually only this worked in my case (maybe because of index/column ambiguity?):

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

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

Reproduce with:

df = pd.DataFrame([[1,2,3,4],[1,2,3,4],[2,3,4,5],[2,3,4,5]], range(4), range(4))

Suuuper belated response, but I think what happened here is that my wording in the example was super confusing. By column, I meant "the name of the column you're searching" but that wasn't at all clear. Updated those snippets, so hopefully it won't trip up anyone else. Thanks for the feedback!

Thank you so much for doing this - it's really, really helpful! You've saved me a couple of hours of head-scratching while wading through various stackoverflow answers.

Incredible work! Thank you for sharing!
I made a VSCode snippet file out of it, if anyone is interested!

Nice list!

I created a way to quickly search and locate pandas snippets. It saves me a ton of time.

https://AllTheSnippets.com/search/

Best.