df=pd.DataFrame({'A': {0:'a', 1:'b', 2:'c'},'B': {0: 1, 1: 3, 2: 5},'C': {0: 2, 1: 4, 2: 6}}) df A B C 0 a1 2 1 b 3 4 2 c 5 6df.melt(id_vars=['A'],value_vars=['B']) A variable value 0 a B1 1 b B 3 2 c B 5 这样,直接看出属性列...
df5.explode("phones") df5 # 原数据没有变 fillna函数 填充缺失值;可以整体填充,也可以对每个属性单独填充 df4 df4.fillna({"sid":"s3","name":"Peter"}) groupby函数 同组统计的功能 图解Pandas的groupby机制 # 借用这个结果 df6 = df5.explode("phones") df6 df6.groupby("sid")["phones"].cou...
pd.concat([df1,df3],axis=1) 1. 2. dropna函数 删除空值:可以对整个DataFrame删除,也可以指定某个属性来删除 df4 = pd.DataFrame({ "sid":["s1","s2", np.nan], "name":["xiaoming",np.nan, "Mike"]}) df4 1. 2. 3. 4. df4.dropna() 1. df4.dropna(subset=["name"]) 1. explode...
df[“列名”]#返回这一列(“列名”)的数据df[[“name”,”age”]]#返回列名为name和 age的两列数据df[‘列字段名’].unique()#显示数据某列的所有唯一值, 有0值是因为对数据缺失值进行了填充df = pd.read_excel(file,skiprows=[2] )#不读取哪里数据,可用skiprows=[i],跳过文件的第i行不读取df.loc...
这是df原来的模样:这是使用了sub( )之后的样子:火眼睛睛的宝宝们,看出来变化了吗?sub( )把列数据依次减掉了NaN,NaN,1,3,5,NaN(之前设的s的值),所有跟NaN计算的数据也都变成了缺失值。比如看“A”列,0-NaN=NaN,1.33-NaN=NaN,-0.76-1=-1.76,0.42-3=-2.58,以此类推。
官方api: http://pandas.pydata.org/pandas-docs/stable/api.html#general-functions pandas是python数据分析的核心模块。它主要提供了五大功能: - 1.支持文件存取操作,支持数据库(sql)、html、json、pickle、csv(txt、excel)、sas、stata、hdf等。 - 2.支持增删改查、切片、高阶函数、分组聚合等单表操作,以及...
df.to_csv('new_purchases.csv') df.to_json('new_purchases.json') df.to_sql('new_purchases', con) Learn Data Science with When we save JSON and CSV files, all we have to input into those functions is our desired filename with the appropriate file extension. With SQL, we’re ...
For each library, the estimation of the memory usage is done with the general syntax below corresponding to the execution with the Vaex library. Also, this focuses only on the memory usage for the data offloading tasks. tm.start() vaex_time = offload_data_with_time('vaex', vaex_df) memo...
>>> df.sort_values( ... by="city08", ... ascending=False, ... kind="mergesort" ... ) city08 cylinders fuelType ... mpgData trany year 2 23 4 Regular ... Y Manual 5-spd 1985 7 23 4 Regular ... Y Automatic 3-spd 1993 8 23 4 Regular ... Y Manual 5-spd 1993 ...
grouped=df.groupby('key1') grouped['data1'].quantile(0.9)# 0.9分位数 key1 a 1.037985 b 0.995878 Name: data1, dtype: float64 To use your own aggregation functions, pass any function that aggregates an array to theaggregateoraggmethod ...