VS Code Snippets File with lots of pandas useful pieces of code

pandas.code-snippets

"pd_to_numeric": {
		"scope": "python",
		"description": "Convert Series datatype to numeric (will error if column has non-numeric values)  ",
		"prefix": "pd_to_numeric",
		"body": [
			"pd.to_numeric(df['Column Name'])"
		],
	},
	"pd_to_numeric_nan": {
		"scope": "python",
		"description": "Convert Series datatype to numeric, changing  non-numeric values to NaN  ",
		"prefix": "pd_to_numeric_nan",
		"body": [
			"pd.to_numeric(df['Column Name'], errors='coerce')"
		],
	},
	"pd_change_type": {
		"scope": "python",
		"description": "Change data type of DataFrame column",
		"prefix": "pd_change_type",
		"body": [
			"df.column_name = df.column_name.astype(np.i,nt64)"
		]
	},
	//Exploring and Finding Data
	"pd_find_dup": {
		"scope": "python",
		"description": "Get a report of all duplicate records in a DataFrame, based on specific columns",
		"prefix": "pd_find_dup",
		"body": [
			"dupes = df[df.duplicated(['col1', 'col2', 'col3'], keep=False)]"
		]
	},
	"pd_dedup": {
		"scope": "python",
		"description": "Remove duplicated rows",
		"prefix": "pd_dedup",
		"body": [
			"dupes = dupes.drop_duplicates()"
		]
	},
	"pd_unique": {
		"scope": "python",
		"description": "List unique values in a DataFrame column  ",
		"prefix": "pd_unique",
		"body": [
			"df['Column Name'].unique()"
		]
	},
	"pd_unique_freq": {
		"scope": "python",
		"description": "For each unique value in a DataFrame column, get a frequency count",
		"prefix": "pd_unique_freq",
		"body": [
			"df['Column Name'].value_counts()"
		]
	},
	"pd_find_by_column": {
		"scope": "python",
		"description": "Grab DataFrame rows where column = a specific value",
		"prefix": "pd_find_by_column",
		"body": [
			"df = df.loc[df.column == 'somevalue']"
		]
	},
	"pd_find_in_a_list": {
		"scope": "python",
		"description": "Grab DataFrame rows where column value is / is not present in a list",
		"prefix": "pd_find_in_a_list",
		"body": [
			"test_data = {'hi': 'yo', 'bye': 'later'}",
			"df = pd.DataFrame(list(d.items()), columns=['col1', 'col2'])",
			"valuelist = ['yo', 'heya']",
			"df[df.col2.isin(valuelist)]"
		]
	},
	"pd_find_by_column_multi": {
		"scope": "python",
		"description": "Select from DataFrame using criteria from multiple columns  ",
		"prefix": "pd_find_by_column_multi",
		"body": [
			"newdf = df[(df['column_one']>2004) & (df['column_two']==9)]"
		]
	},
	"pd_iterrows": {
		"scope": "python",
		"description": "Loop through rows in a DataFrame (if you must)",
		"prefix": "pd_iterrows",
		"body": [
			"for index, row in df.iterrows():",
			"\tprint (index, row['some column'])"
		]
	},
	"pd_itertuples": {
		"scope": "python",
		"description": "Much faster way to loop through DataFrame rows if you can work with tuples  ",
		"prefix": "pd_itertuples",
		"body": [
			"for row in df.itertuples():",
			"\tprint(row)"
		]
	},
	"pd_top5": {
		"scope": "python",
		"description": "Get top n for each group of columns in a sorted DataFrame (make sure DataFrame is sorted first)",
		"prefix": "pd_top5",
		"body": [
			"top5 = df.groupby(['groupingcol1','groupingcol2']).head(5)"
		]
	},
	"pd_find_null": {
		"scope": "python",
		"description": "Grab DataFrame rows where specific column is null/notnull",
		"prefix": "pd_find_null",
		"body": [
			"newdf = df[df['column'].isnull()]"
		]
	},
	"pd_find_multi_index": {
		"scope": "python",
		"description": "Select from DataFrame using multiple keys of a hierarchical index",
		"prefix": "pd_find_multi_index",
		"body": [
			"df.xs(('index level 1 value','index level 2 value'), level=('level 1','level 2'))"
		]
	},
	"pd_slice": {
		"scope": "python",
		"description": "Slice values in a DataFrame column (aka Series)",
		"prefix": "pd_slice",
		"body": [
			"df.column.str[0:2]"
		]
	},
	"pd_count_rows": {
		"scope": "python",
		"description": "Get quick count of rows in a DataFrame",
		"prefix": "pd_count_rows",
		"body": [
			"len(df.index)"
		]
	},
	"pd_len_col": {
		"scope": "python",
		"description": "Get length of data in a DataFrame column",
		"prefix": "pd_len_col",
		"body": [
			"df.column_name.str.len()"
		]
		//Updating and Cleaning Data
	},
	"pd_del_col": {
		"scope": "python",
		"description": "Delete column from DataFrame",
		"prefix": "pd_del_col",
		"body": [
			"del df['column']"
		]
	},
	"pd_drop_col": {
		"scope": "python",
		"description": "Drop column from DataFrame",
		"prefix": "pd_rop_col",
		"body": [
			"df.drop(column=[])"
		]
	},
	"pd_rename_columns": {
		"scope": "python",
		"description": "Rename several DataFrame columns",
		"prefix": "pd_rename_columns",
		"body": [
			"df = df.rename(columns = {",
			"\t'col1_old_name':'col1_new_name',",
			"\t'col2_old_name':'col2_new_name',",
			"\t'col3_old_name':'col3_new_name',",
			"\t})"
		]
	},
	"pd_lower_all_colname": {
		"scope": "python",
		"description": "Lower-case all DataFrame column names",
		"prefix": "pd_lower_all_colname",
		"body": [
			"df.columns = map(str.lower, df.columns)"
		]
	},
	"pd_lower_all": {
		"scope": "python",
		"description": "Lower-case everything in a DataFrame column",
		"prefix": "pd_lower_all",
		"body": [
			"df.column_name = df.column_name.str.lower()"
		]
	},
	"pd_sort_mult": {
		"scope": "python",
		"description": "Sort DataFrame by multiple columns",
		"prefix": "pd_sort_mult",
		"body": [
			"df = df.sort_values(",
			"\t'col1','col2','col3'],ascending=[1,1,0])"
		]
	},
	"pd_nan2none": {
		"scope": "python",
		"description": "Change all NaNs to None (useful before loading to a db)",
		"prefix": "pd_nan2none",
		"body": [
			"df = df.where((pd.notnull(df)), None)"
		]
	},
	"pd_db_clean": {
		"scope": "python",
		"description": "More pre-db insert cleanup...make a pass through the DataFrame, stripping whitespace from strings and changing any empty values to `None`  ",
		"prefix": "pd_db_clean",
		"body": [
			"df = df.applymap(lambda x: str(x).strip() if len(str(x).strip()) else None)"
		]
	},
	"pd_clean_nonNumeric": {
		"scope": "python",
		"description": "Get rid of non-numeric values throughout a DataFrame:",
		"prefix": "pd_clean_nonNumeric",
		"body": [
			"for col in refunds.columns.values:",
			"\trefunds[col] = refunds[col].replace(",
			"\t'[^0-9]+.-', '', regex=True)"
		]
	},
	"pd_colVal_if": {
		"scope": "python",
		"description": "Set DataFrame column values based on other column values  ",
		"prefix": "pd_colVal_if",
		"body": [
			"df.loc[(df['column1'] == some_value) & (df['column2'] == some_other,_value), ['column_to_change']] = new_value"
		]
	},
	"pd_cleanMissing": {
		"scope": "python",
		"description": "Clean up missing values in multiple DataFrame columns",
		"prefix": "pd_cleanMissing",
		"body": [
			"df = df.fillna(",
			"\t'col1': 'missing',",
			"\t'col2': '99.999',",
			"\t'col3': '999',",
			"\t'col4': 'missing',",
			"\t'col5': 'missing',",
			"\t'col6': '99'})"
		]
	},
	"pd_swap0_with_null": {
		"scope": "python",
		"description": "Doing calculations with DataFrame columns that have missing values. In example below, swap in 0 for df['col1'] cells that contain null.",
		"prefix": "pd_swap0_with_null",
		"body": [
			"df['new_col'] = np.where(",
			"\tpd.isnull(df['col1']), 0, df['col1']) + df['col2']"
		]
	},
	"pd_split_delimeted": {
		"scope": "python",
		"description": "Split delimited values in a DataFrame column into two new columns  ",
		"prefix": "pd_split_delimeted",
		"body": [
			"df['new_col1'], df['new_col2'] = zip(",
			"\t*df['original_col'].apply(",
			"\tlambda x: x.split(': ', 1)))"
		]
	},
	"pd_collapse_hier": {
		"scope": "python",
		"description": "Collapse hierarchical column indexes",
		"prefix": "pd_collapse_hier",
		"body": [
			"df.columns = df.columns.get_level_values(0),"
		]
	},
	//Reshaping, Concatenating, and Merging Data
	"pd_pt": {
		"scope": "python",
		"description": "Pivot data (with flexibility about what what becomes a column and what stays a row).  ",
		"prefix": "pd_pt",
		"body": [
			"pd.pivot_table(",
			"\tdf,values='cell_value',",
			"\tindex=['col1', 'col2', 'col3'], #these stay as columns; will fail silently if any of these cols have null values",
			"\tcolumns=['col4']) #data values in this column become their own column",
			"\taggfunc='count',"
		]
	},
	"pd_concat_1col": {
		"scope": "python",
		"description": "Concatenate two DataFrame columns into a new, single column  ",
		"prefix": "pd_concat_1col",
		"body": [
			"df['newcol'] = df['col1'].astype(str) + df['col2'].astype(str)"
		]
	},
	"pd_merge": {
		"scope": "python",
		"description": "Merge two DataFrame into a new - leftjoin or vlookup",
		"prefix": "pd_merge",
		"body": [
			"df1 = pd.merge(",
				"\tdf1,",
				"\tdf2,",
				"\thow='left',",
				"\tleft_on=[],",
				"\tright_on=[],",
				"\tindicator=True,)"
		]
	},
	//Display and formatting
	"pd_3Decimal": {
		"scope": "python",
		"description": "Set up formatting so larger numbers aren't displayed in scientific notation  ",
		"prefix": "pd_3Decimal",
		"body": [
			"pd.set_option('display.float_format', lambda x: '%.3f' % x)"
		]
	},
	"pd_": {
		"scope": "python",
		"description": "To display with commas and no decimals",
		"prefix": "pd_comma",
		"body": [
			"pd.options.display.float_format ='{:,.0f}'.format"
		]
	},
	//Creating DataFrames
	"pd_dict2df": {
		"scope": "python",
		"description": "Create a DataFrame from a Python dictionary",
		"prefix": "pd_dict2df",
		"body": [
			"df = pd.DataFrame(list(a_dictionary.items()), columns = ['column1', 'column2'])"
		]
	},
	"pd_django2df": {
		"scope": "python",
		"description": "Convert Django queryset to DataFrame",
		"prefix": "pd_django2df",
		"body": [
			"qs = DjangoModelName.objects.all()",
			"\tq = qs.values()",
			"\tdf = pd.DataFrame.from_records(q)"]
		},
	//Manipulate CSV
	"pd_2csv": {
		"scope": "python",
		"description": "Export df to CSV",
		"prefix": "pd_2csv",
		"body": ["df.to_csv(str(Path.cwd() / f'{}.csv'), index=False, sep=';')"]
		},
	"pd_load_csv": {
		"scope": "python",
		"description": "Load CSV to df",
		"prefix": "pd_load_csv",
		"body": ["df = pd.read_csv(str(Path.cwd() / f'{}.csv'), sep=';')"]
		}