import pandas as pd # creating series (one-dimensional array) simple_series = pd.Series([42, 55, 73], dtype='f8') simple_series # 0 42.0 # 1 55.0 # 2 73.0 # dtype: float64 # creating series with specified index index_series = pd.Series([42, 55, 73], index=["electron", "proton", "neutron"], dtype='f8') index_series # electron 42.0 # proton 55.0 # neutron 73.0 # dtype: float64 # accessing value in series index_series['electron'] # 42.0 # accessing multiple values in series (range) index_series['electron':'neutron'] # electron 42.0 # proton 55.0 # neutron 73.0 # dtype: float64 # accessing multiple values in series using indexing index_series[1:] # proton 55.0 # neutron 73.0 # dtype: float64 # creating series from dictionary dict_series = pd.Series({'electron': 6, 'neutron': 28, 'proton': 496, 'neutrino': 8128}) dict_series # electron 6 # neutrino 8128 # neutron 28 # proton 496 # dtype: int64 # combining two series into one column index_series + dict_series # electron 48.0 # neutrino NaN # neutron 101.0 # proton 551.0 # dtype: float64 # combining two series as dataframe combined_series = pd.DataFrame({'A': index_series, 'B': dict_series}) combined_series # A B # electron 42.0 6 # neutrino NaN 8128 # neutron 73.0 28 # proton 55.0 496 # accessing columns (as series) combined_series['A'] # electron 42.0 # neutrino NaN # neutron 73.0 # proton 55.0 # Name: A, dtype: float64 # add new row with index append_series = combined_series.append(pd.DataFrame({'A': [-8128]}, index=['antineutrino'])) append_series # A B # electron 42.0 6.0 # neutrino NaN 8128.0 # neutron 73.0 28.0 # proton 55.0 496.0 # antineutrino -8128.0 NaN # drop row append_series = append_series.drop('neutron') append_series # A B # electron 42.0 6.0 # neutrino NaN 8128.0 # proton 55.0 496.0 # antineutrino -8128.0 NaN # transpose combined_series.T # electron neutrino neutron proton # A 42.0 NaN 73.0 55.0 # B 6.0 8128.0 28.0 496.0 # masking combined_series > 120 # A B # electron False False # neutrino False True # neutron False False # proton False True # add masking as column (e.g. larger than 120) combined_series['large'] = (combined_series['A'] > 120) | (combined_series['B'] > 120) combined_series # A B large # electron 42.0 6 False # neutrino NaN 8128 True # neutron 73.0 28 False # proton 55.0 496 True # delete a column del combined_series['large'] combined_series # A B # electron 42.0 6 # neutrino NaN 8128 # neutron 73.0 28 # proton 55.0 496