import pandas as pd
df1 = pd.read_csv(
'123.csv'
,encoding =
'utf-8'
)
print
(df1)
# directly show dataframe 直接檢視
print
(df1.info)
#dataframe's basic information 資訊描述
print
(df1.head(10)
)#glance over top 10 records 看前十行
print
(df1.tail(10)
)#glance over last 10 records 看後十行
print
(df1.columns)
#show columns' name 列印所有列名稱
df1.columns =
['storename'
,'star'
,'consumption'
,'address'
,'phone'
]print
(df1.columns)
df2 = df1.rename(
columns =
,#note wrong name will be ignored 錯誤或者沒有的列名將忽略不報錯
inplace =
false
#if or not directly replace dataframe
)print
(df2)
'var'
] single columns outcome is series,use list can change it to df
df[[
'var1'
,'var2'
]] multi-columns' usage,var not exit
del df[
'columns-name'
]# directly delet df columns
print
(df2[
['stars'
,'phone']]
)#delet columns
df.drop(
index/columns = row/columns' label ready to delet ,more than one use list
inplace =
false
)#刪除行和列
print
(df2.drop(columns =
['stars'
,'phone'])
)print
(df2.drop(index =[0
,1])
)
pandas' data type
:#所支援的型別
float
,int
,string,
bool
,datetime64[ns]
,datetime64[ns,tz]
,timedelta[ns]
category,
object
print
(df2.dtypes)
df2.astype(
dtype:assign new type you want (
int/
float
/bool
/str
) copy:same to inplace;
true
is default
erroes =
'raise':if
ornot
raise errors [ignore/
raise])
'''print
(df2.astype(
'str'
).dtypes)
#所有列轉換為str
print
(df2.stars.astype(
'float'))
#列stars轉換為float
print
(df2.stars.astype(
'float'
).dtypes)
print
(df2[
['stars'
,'phone']]
.astype(
'str'
).dtypes)列stars和phone轉換為str
print
(df2[
['stars'
,'phone']]
.astype(
'str').
(pd.to_numeric,errors =
'ignore'
).dtypes)
df3 = pd.dataframe(
)df = df3.iloc[1:
]print
(df )
print
(df.dtypes)
print
(df3.infer_objects(
).dtypes)
#auto-convert types 自動識別型別並轉換，經測試並不好用
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