/
pandas_map_replace.py
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/
pandas_map_replace.py
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import pandas as pd
df = pd.read_csv('data/src/sample_pandas_normal.csv')
print(df)
# name age state point
# 0 Alice 24 NY 64
# 1 Bob 42 CA 92
# 2 Charlie 18 CA 70
# 3 Dave 68 TX 70
# 4 Ellen 24 CA 88
# 5 Frank 30 NY 57
s = df['state']
print(s)
# 0 NY
# 1 CA
# 2 CA
# 3 TX
# 4 CA
# 5 NY
# Name: state, dtype: object
s_map_all = s.map({'NY': 'NewYork', 'CA': 'California', 'TX': 'Texas'})
print(s_map_all)
# 0 NewYork
# 1 California
# 2 California
# 3 Texas
# 4 California
# 5 NewYork
# Name: state, dtype: object
s_replace_all = s.replace({'NY': 'NewYork', 'CA': 'California', 'TX': 'Texas'})
print(s_replace_all)
# 0 NewYork
# 1 California
# 2 California
# 3 Texas
# 4 California
# 5 NewYork
# Name: state, dtype: object
s_map = s.map({'NY': 'NewYork'})
print(s_map)
# 0 NewYork
# 1 NaN
# 2 NaN
# 3 NaN
# 4 NaN
# 5 NewYork
# Name: state, dtype: object
s_replace = s.replace({'NY': 'NewYork'})
print(s_replace)
# 0 NewYork
# 1 CA
# 2 CA
# 3 TX
# 4 CA
# 5 NewYork
# Name: state, dtype: object
s_copy = s.copy()
s_copy.update(s_copy.map({'NY': 'NewYork'}))
print(s_copy)
# 0 NewYork
# 1 CA
# 2 CA
# 3 TX
# 4 CA
# 5 NewYork
# Name: state, dtype: object
s_copy = s.copy()
s_copy.replace({'NY': 'NewYork'}, inplace=True)
print(s_copy)
# 0 NewYork
# 1 CA
# 2 CA
# 3 TX
# 4 CA
# 5 NewYork
# Name: state, dtype: object
s_map_num = s.map({'NY': 0, 'CA': 1, 'TX': 2})
print(s_map_num)
# 0 0
# 1 1
# 2 1
# 3 2
# 4 1
# 5 0
# Name: state, dtype: int64
df['state'] = df['state'].map({'NY': 0, 'CA': 1, 'TX': 2})
print(df)
# name age state point
# 0 Alice 24 0 64
# 1 Bob 42 1 92
# 2 Charlie 18 1 70
# 3 Dave 68 2 70
# 4 Ellen 24 1 88
# 5 Frank 30 0 57
print(df['state'].dtype)
# int64