- dataframe from records:
pd.DataFrame.from_records([['A', 4], ['B', 2]], columns=['g', 'legs'], index=['g']) - (un)pickle:
orders.to_pickle('saved.pkl'); pd.read_pickle('saved.pkl') - filter data frame per (part of) index:
frame.loc[580] - timestamp:
pd.Timestamp('2017-05-01') - random sample:
frame.sample(n=20, random_state=42) - columns list:
list(frame.columns) - rename columns:
frame.rename({'from': 'to'}) - drop columns:
frame.drop(columns=['colname']) - frame of bools:
frame['col'] > 5 - filter:
frame[frame_of_bools & more_bools] - remove rows with null/NaN values:
frame.dropna()(one null is enough to trigger the filter) - aggregation:
frame.groupby('colname').agg(['count', 'min', 'max']) - merge on index:
frame.merge(another_frame, left_index=True, right_index=True) - merge on index via join:
frame.join(another_frame, how='inner') - index changes:
frame.set_index(['col1', 'col2']),frame.reset_index() - only rows where index is in given set:
frame.filter(items=[458, 580], axis='index') - regex replace in string series
series.str.replace(r'foo(.*)', r'bar\1', case=False) - per-row mapping:
frame.apply(lambda row: pd.Series([row[0] + 1], index=[row.index[0]]), axis='columns')frame.apply(lambda row: [row[0] + row[1]], axis='columns', result_type='expand')
- mapping in a series:
series.map(lambda x: x + 5) - don't truncate table display:
pd.set_option('display.max_colwidth', -1) - display table with HTML:
IPython.core.display.HTML(frame.to_html(escape=False)) - sort by column:
frame.sort_values(['col1', 'col2'], ascending=True)
- item labels:
mi.labels->[[1,1,2], ['blue', 'red', 'blue']] - unique labels for each level:
mi.levels->[[1,2,3], ['blue', 'red']] - column names:
mi.names->['id', 'color']