Series

• Subclass of numpy.ndarray
• Data = any type
• Index labels need not be ordered
• Duplicates are possible (but result in reduced functionality)

from pandas import Series, DataFrame
import pandas as pd

list_data=[1,2,3,4,5]
list_name=['a','b','c','d','e']
example_obj=Series(list_data, list_name, dtype=np.float32, name="example")

dict_data={'e':6,'f':7,'g':8,'h':9,'i':10}
example_obj2=Series(dict_data)
print(f"initializing series with list: \\n{example_obj}")
print(f"initializing series with dict: \\n{example_obj2}")

#initializing series with list: 
#a    1.0
#b    2.0
#c    3.0
#d    4.0
#e    5.0
#Name: example, dtype: float32
#initializing series with dict: 
#e     6
#f     7
#g     8
#h     9
#i    10
#dtype: int64

print(example_obj["a"])  # find values by index
example_obj["a"] = 
example_obj.values  #value list
example_obj.index   #index list             #  1    1.0     Just like df, 
dict_data={1:1,2:2}                         #  2    2.0     the index is the 
indexes=[1,2,3,4,5]                         #  3    NaN     standard
series= Series(dict_data, index=indexes)    #  4    NaN
                                            #  5    NaN

DataFrame

DataFrame indexing

• loc → index location ( call by index name)
• iloc → index position ( call by index)

DataFrame handling

• column에 새로운 데이터 할당

df.dept = df.age > 40  #boolean

• df.to_csv, df.index, df.values
• df.drop(”dept”,axis=1) → returning a DataFrame that dosen’t have column “dept”
• del df[”dept”] → deleting the column “dept” in df

Selection & Drop