{"id":513,"date":"2019-07-06T23:18:00","date_gmt":"2019-07-06T15:18:00","guid":{"rendered":"http:\/\/note.systw.net\/note\/?p=513"},"modified":"2023-11-03T23:20:01","modified_gmt":"2023-11-03T15:20:01","slug":"python-pandas-view","status":"publish","type":"post","link":"https:\/\/systw.net\/note\/archives\/513","title":{"rendered":"Python pandas view"},"content":{"rendered":"\n

\u6aa2\u8996dataframe\u72c0\u6cc1<\/p>\n\n\n\n

………………….<\/p>\n\n\n\n

\u986f\u793a\u8cc7\u6599\u6982\u6cc1<\/strong><\/h2>\n\n\n\n

\u986f\u793a\u8cc7\u6599\u7dad\u5ea6(\u65e5\u8a8c\u7b46\u6578\u548c\u6b04\u4f4d\u6578\u91cf)<\/strong>
df.shape
ex:
(100, 5)<\/p>\n\n\n\n

\u986f\u793a\u8cc7\u6599\u524d10\u884c<\/strong>
df.head()
ex:
\u53ea\u986f\u793a3\u7b46
df.head(3)<\/p>\n\n\n\n

\u8cc7\u6599\u63cf\u8ff0<\/strong>
\u4e86\u89e3data\u88e1\u9762\u7684\u72c0\u614b
df.describe(include=’all’)
ex:
categorical numeric object
count 3 3.0 3
unique 3 NaN 3
top f NaN c
freq 1 NaN 1
mean NaN 2.0 NaN
std NaN 1.0 NaN
min NaN 1.0 NaN
25% NaN 1.5 NaN
50% NaN 2.0 NaN
75% NaN 2.5 NaN
max NaN 3.0 NaN<\/p>\n\n\n\n


\u986f\u793a\u6240\u6709\u6b04\u4f4d\u540d\u7a31<\/strong>
df.columns.values
df.columns<\/p>\n\n\n\n

\u986f\u793a\u6240\u6709\u6b04\u4f4d\u7684\u985e\u578b<\/strong>
df.dtypes<\/p>\n\n\n\n

\u986f\u793a\u7279\u5b9a\u6b04\u4f4d<\/strong>
df.columns[ ]
ex:
\u986f\u793a\u6b04\u4f4d0\u7684\u540d\u7a31
print df.columns[0]<\/p>\n\n\n\n

\u986f\u793a\u7279\u5b9a\u5217\u6578<\/strong>
df[ < start>:< end> ]
ex:
\u986f\u793a\u7b2c10\u5217\u5230\u7b2c11\u5217\u7684\u8cc7\u6599
df[10:11]<\/p>\n\n\n\n

\u8cc7\u6599\u6392\u5e8f<\/strong>
sort_values( < fieldset > [,paramater] )
ex:
dataphoto.sort_values(by=’field1′,ascending=True)
ex:
dataphoto[‘field1’].sort_values(ascending=True)
ex:
df.groupby(‘job’).sort_values([‘job’,’count’],ascending=False)
ps:
\u53c3\u6578\u52a0 inplace=True\u53ef\u4ee5\u5132\u5b58\u6392\u5e8f\u7d50\u679c
…<\/p>\n\n\n\n

\u986f\u793a\u8cc7\u6599\u7d71\u8a08\u7d50\u679c<\/strong><\/h2>\n\n\n\n

\u8a08\u7b97\u5e73\u5747\u503c<\/strong>
df[< field name >].mean()
ex:
> df[\u2018byte’].mean()<\/p>\n\n\n\n


\u5217\u51fa\u8a72\u6b04\u4f4d\u6709\u5c11\u6578\u91cf<\/strong>
df[< field name >].count()<\/p>\n\n\n\n

\u5217\u51fa\u552f\u4e00\u7684\u503c\u548c\u51fa\u73fe\u6b21\u6578<\/strong>
df[< field name >].value_counts()
ex:
> df[\u2018protocol’].value_counts()
Tcp: 523
TCP: 52
udp: 45
UDP: 12<\/p>\n\n\n\n


\u4e86\u89e3\u6bcf\u500b\u6b04\u4f4d\u7a7a\u503c\u7684\u6578\u91cf<\/strong>
> df.isnull().sum()
ex:
df.isnull().sum()
ipv4 52
ipv6 129
protocol 0
port 0<\/p>\n\n\n\n

\u5217\u51fa\u552f\u4e00\u7684\u503c<\/strong>
df[< field name >].unique()<\/p>\n\n\n\n


\u5217\u51fa\u4e00\u4e9b\u7d71\u8a08\u8cc7\u8a0a<\/strong>
df[< field name >].describe()<\/p>\n\n\n\n


….
\u7528groupby\u770b\u8cc7\u6599<\/p>\n\n\n\n

size()<\/strong>
\u7528size()\u770bsector\u88e1\u9762\u6bcf\u500b\u7d44\u5225\u5167\u7684\u5927\u5c0f
ex:
sector = df.groupby(“fileid”)
print sector.size()<\/p>\n\n\n\n

count(*)<\/strong>
df.groupby(“< group by column>”).count()
ex:
print df.groupby(“fileid”).count()<\/p>\n\n\n\n

count(distinct column)<\/strong>
df.groupby(“< group by column>”).< column>.nunique()
ex:
print df.groupby(“fileid”).userid.nunique()<\/p>\n\n\n\n

average<\/strong>
ex:
pddata2=pddata.groupby(by=’fileid’).mean()<\/p>\n\n\n\n

\u5176\u4ed6groupby\u652f\u63f4\u7684\u529f\u80fd
max,min,sum,std,median<\/p>\n\n\n\n

…<\/p>\n\n\n\n

\u641c\u5c0b\u8cc7\u6599 <\/strong><\/h2>\n\n\n\n

\u9078\u64c7\u6307\u5b9a\u689d\u4ef6\u7684\u884c,\u5b8c\u6574\u6bd4\u5c0d<\/strong>
ex:
df[ df.name != ‘Tina’ ]<\/p>\n\n\n\n

\u9078\u64c7\u6307\u5b9a\u689d\u4ef6\u7684\u884c,\u7ce2\u7cca\u6bd4\u5c0d<\/strong>
.str.contains( < string> )
ex:
df[ df[\u2018filed1′].str.contains(‘@’,na = False) ]<\/p>\n\n\n\n

\u9078\u53d6\u7279\u5b9a\u6b04\u4f4d\u975enull\u7684\u884c<\/strong>
< column name>.notnull()
ex:
data[data[‘\u653b\u64ca\u8005\u4f7f\u7528\u8005\u540d\u7a31’].notnull()]<\/p>\n\n\n\n

\u9078\u53d6\u7279\u5b9a\u6b04\u4f4d\u70banull\u7684\u884c<\/strong>
< column name>.isnull()
ex:
data[data[‘\u653b\u64ca\u8005\u4f7f\u7528\u8005\u540d\u7a31’].isnull()]<\/p>\n\n\n\n

\u9078\u53d6\u7279\u5b9a\u578b\u614b\u7684\u6b04\u4f4d<\/strong>
select_dtypes()
ex:
df.select_dtypes(include=[‘int64’])
df.select_dtypes(exclude=[object])<\/p>\n","protected":false},"excerpt":{"rendered":"

\u6aa2\u8996dataframe\u72c0\u6cc1 ……&# …<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"fifu_image_url":"","fifu_image_alt":"","_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":false,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[14],"tags":[],"class_list":["post-513","post","type-post","status-publish","format-standard","hentry","category-develop"],"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"jetpack-related-posts":[{"id":511,"url":"https:\/\/systw.net\/note\/archives\/511","url_meta":{"origin":513,"position":0},"title":"Python pandas access","author":"raymond","date":"2019 \u5e74 7 \u6708 6 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