pandas 1. basic and attributes
pandas 1. basic and attributes
https://pandas.pydata.org/docs/ 참고
pandas 1. basic and attributes
예제 5_1
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'''
pandas Series : basic and attributes
'''
import numpy as np
import pandas as pd
def testPd1():
ser_a = pd.Series(
np.array([1,2,3,4,5]), # data
index=['a','b','c','d','e'], # index - key값은 다 달라야 한다
dtype=np.float64) # element data type
#print('series a[]', ser_a, sep='\n')
#print('The index structure')
#print(ser_a.index)
print('1:', ser_a[1])
print('2:', ser_a['b'])
# attributes
print('ndim',ser_a.ndim,'size',ser_a.size,'nbytes',ser_a.nbytes,'shape',ser_a.shape,sep='\n')
print('values :',ser_a.values)
print('empty :',ser_a.empty)
# at, iat, loc, iloc
print('1-th element:',ser_a.at['a'])
print('1-th element:',ser_a.iat[1])
print('group element:',ser_a.loc[:'c'],sep='\n')
print('group element:',ser_a.iloc[:2],sep='\n')
# index가 없는 경우
ser_a1 = pd.Series(['1','2','3','4'])
print(ser_a1[0])
print(ser_a1.at[2])
print(ser_a1.iat[2]) # 기본값이 숫자로 01234인듯?
return None
def testPd2():
df_A = pd.DataFrame(
[[1,2,3,4],[2,3,4,5]], # data
#index=['1st','2nd'], # index(row)
columns=['col1','col2','col3','col4'] # columns
)
print('df_A :\n',df_A)
# implicit
# print(df_A[1])
print(df_A['col2']) # col 단위로 먼저 잘라야된다
# Series를 가져오게 된다
print(df_A['col2'][1]) #Series로 가져오면 인덱스로 접근 가능하지만
# explicit
#print(df_A.at['1st','col2'])
print(df_A.loc[:,:'col2'].iat[1,1])
# attribute임에 유의 - 결과가 값이다. doc 참고
# index도 attribute - 결과가 값이다.
print(df_A.index) # range index..
return None
def main():
testPd1()
print('---------------')
testPd2()
return None
if __name__ == '__main__':
main()
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1: 2.0
2: 2.0
ndim
1
size
5
nbytes
40
shape
(5,)
values : [1. 2. 3. 4. 5.]
empty : False
1-th element: 1.0
1-th element: 2.0
group element:
a 1.0
b 2.0
c 3.0
dtype: float64
group element:
a 1.0
b 2.0
dtype: float64
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3
---------------
df_A :
col1 col2 col3 col4
0 1 2 3 4
1 2 3 4 5
0 2
1 3
Name: col2, dtype: int64
3
3
RangeIndex(start=0, stop=2, step=1)
예제 5_2
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import numpy as np
import pandas as pd
# read a CSV file => inject the CSV file into a Dataframe
def testpd1():
# new data type in form of dictionary
dt_crimes = {
# 'column_name' : data_type
'cdatetime': np.str_,
'address': np.str_,
'district':np.int8,
'beat':np.object_,
'grid':np.str_,
'crimedescr':np.str_,
'ucr_ncic_code':"S4",
'latitude':np.float32,
'longitude':np.float32
}
crime_csv_path = "./SacramentocrimeJanuary2006.csv"
df_csv = pd.read_csv(crime_csv_path,dtype=dt_crimes,sep=",")
print('head :\n',df_csv.head())
# 유닉스 명령어 head tail default 5줄
print(df_csv.dtypes)
#print('Dataframd Size = ',df_csv.memory_usage())
#print(df_csv.size)
print('length =',df_csv.iloc[0,:].nbytes) # Series 의 attribute
return None
def testpd2():
csv_path = './ratings.csv'
dt_ratings = {
# userId,movieId,rating,timestamp
'userId':'S8',
'movieId':'S8',
'rating':np.float16,
'timestamp':np.int64
}
df_ratings = pd.read_csv(csv_path,dtype=dt_ratings)
#print(df_ratings.tail())
#print(df_ratings.dtypes)
print(df_ratings.index)
print(df_ratings.columns)
row_1 = df_ratings.iloc[1]
print('row_1 :\n',row_1)
print('row_1[1] :\n',row_1[1]) # 한줄이라서 Series로 변해서 가능. 그러나 쓰지말것. iat iloc 쓰기
print('rating' in row_1)
print(row_1['rating'])
return None
def testpd3():
crime_xls_file = "./SacramentocrimeJanuary2006.xls"
df_csv = pd.read_excel(crime_xls_file)
print('head :\n',df_csv.head())
return None
def main():
testpd1()
print('--------------')
testpd2()
print('--------------')
testpd3()
return None
if __name__ == '__main__':
main()
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head :
cdatetime address district ... ucr_ncic_code latitude longitude
0 1/1/06 0:00 3108 OCCIDENTAL DR 3 ... b'2404' 38.550419 -121.391418
1 1/1/06 0:00 2082 EXPEDITION WAY 5 ... b'2204' 38.473499 -121.490189
2 1/1/06 0:00 4 PALEN CT 2 ... b'2404' 38.657845 -121.462097
3 1/1/06 0:00 22 BECKFORD CT 6 ... b'2501' 38.506775 -121.426949
4 1/1/06 0:00 3421 AUBURN BLVD 2 ... b'2299' 38.637447 -121.384613
[5 rows x 9 columns]
cdatetime object
address object
district int8
beat object
grid object
crimedescr object
ucr_ncic_code object
latitude float32
longitude float32
dtype: object
length = 72
--------------
RangeIndex(start=0, stop=25000095, step=1)
Index(['userId', 'movieId', 'rating', 'timestamp'], dtype='object')
row_1 :
userId b'1'
movieId b'306'
rating 3.5
timestamp 1147868817
Name: 1, dtype: object
row_1[1] :
b'306'
True
3.5
--------------
head :
cdatetime address district ... ucr_ncic_code latitude longitude
0 2001-01-06 00:00:00 3108 OCCIDENTAL DR 3 ... 2404 38.550420 -121.391416
1 2001-01-06 00:00:00 2082 EXPEDITION WAY 5 ... 2204 38.473501 -121.490186
2 2001-01-06 00:00:00 4 PALEN CT 2 ... 2404 38.657846 -121.462101
3 2001-01-06 00:00:00 22 BECKFORD CT 6 ... 2501 38.506774 -121.426951
4 2001-01-06 00:00:00 3421 AUBURN BLVD 2 ... 2299 38.637448 -121.384613
[5 rows x 9 columns]
예제 5_3 (loc,iloc,at,iat)
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import numpy as np
import pandas as pd
def testpd1():
#crime_loc
crime_loc = {
'latitude':[38.65042047,
37.47350069,
38.65784584,
38.50677377],
'longitude':[-121.3914158,
-121.4901858,
-121.4621009,
-121.4269508]
}
#df_crime_loc
df_crime_loc = pd.DataFrame(crime_loc)
#print(df_crime_loc)
#print(df_crime_loc.head())
print(df_crime_loc.describe())
print('')
'''
sr_df_crime_loc_lat = df_crime_loc['latitude']
# descriptive statistics
# min max mean std mode median
sr_min = sr_df_crime_loc_lat.min()
print(sr_min)
sr_mean = sr_df_crime_loc_lat.mean()
print(sr_mean)
sr_mode = sr_df_crime_loc_lat.mode()
print(sr_mode)
sr_median = sr_df_crime_loc_lat.median()
print(sr_median)
# 행을 가져오기
tmp = df_crime_loc.iloc[1]
print(tmp.mean())
'''
# list the ranking
print(df_crime_loc[['latitude']].rank())
# crime location.latitude > 38.3
# df_crime_loc['check'] = df_crime_loc[['latitude']] > 38.6 # 열 하나를 추가한다.
df_crime_loc_tag = df_crime_loc[['latitude']] > 38.6
print(df_crime_loc_tag)
print(df_crime_loc_tag.any())
# result
print(df_crime_loc[df_crime_loc_tag])
return None
def main():
testpd1()
return None
if __name__ == '__main__':
main()
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latitude longitude
count 4.000000 4.000000
mean 38.322135 -121.442663
std 0.570013 0.042854
min 37.473501 -121.490186
25% 38.248456 -121.469122
50% 38.578597 -121.444526
75% 38.652277 -121.418067
max 38.657846 -121.391416
latitude
0 3.0
1 1.0
2 4.0
3 2.0
latitude
0 True
1 False
2 True
3 False
latitude True
dtype: bool
latitude longitude
0 38.650420 NaN
1 NaN NaN
2 38.657846 NaN
3 NaN NaN
예제 5_4 (trim)
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import numpy as np
import pandas as pd
# read a CSV file => inject the CSV file into a Dataframe
def testpd1():
crime_csv_path = "./SacramentocrimeJanuary2006.csv"
df_csv = pd.read_csv(crime_csv_path,sep=",")
#print(df_crime)
#print(df_csv.head())
# trim a Series named 'address'
sr_x = df_csv[['address']]
print(sr_x)
#trim a DataFrame named 'address, crimedescr, grid'
y = df_csv[['address','crimedescr','grid']]
print(y)
return None
def main():
testpd1()
return None
if __name__ == '__main__':
main()
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address
0 3108 OCCIDENTAL DR
1 2082 EXPEDITION WAY
2 4 PALEN CT
3 22 BECKFORD CT
4 3421 AUBURN BLVD
... ...
7579 26TH ST / G ST
7580 4011 FREEPORT BLVD
7581 30TH ST / K ST
7582 5303 FRANKLIN BLVD
7583 COBBLE COVE LN / COBBLE SHORES DR
[7584 rows x 1 columns]
address crimedescr grid
0 3108 OCCIDENTAL DR 10851(A)VC TAKE VEH W/O OWNER 1115
1 2082 EXPEDITION WAY 459 PC BURGLARY RESIDENCE 1512
2 4 PALEN CT 10851(A)VC TAKE VEH W/O OWNER 212
3 22 BECKFORD CT 476 PC PASS FICTICIOUS CHECK 1443
4 3421 AUBURN BLVD 459 PC BURGLARY-UNSPECIFIED 508
... ... ... ...
7579 26TH ST / G ST 594(B)(2)(A) VANDALISM/ -$400 728
7580 4011 FREEPORT BLVD 459 PC BURGLARY BUSINESS 957
7581 30TH ST / K ST TRAFFIC-ACCIDENT INJURY 841
7582 5303 FRANKLIN BLVD 3056 PAROLE VIO - I RPT 969
7583 COBBLE COVE LN / COBBLE SHORES DR TRAFFIC-ACCIDENT-NON INJURY 1294
[7584 rows x 3 columns]
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