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