我的桌子如下:
import pandas as pd
import numpy as np
#simple table
fazenda = [6010,6010,6010,6010]
quadra = [1,1,2,2]
talhao = [1,2,3,4]
arTotal = [32.12,33.13,34.14,35.15]
arCarr = [i/2 for i in arTotal]
arProd = [i/2 for i in arTotal]
varCan = ['RB1','RB2','RB3','RB4']
d
我确信我在这里的课堂上遗漏了一些东西,但基本上这是我的课:
import pandas as pd
import numpy as np
import scipy
#example DF with OHLC columns and 100 rows
gold = pd.DataFrame({'Open':[i for i in range(100)],'Close':[i for i in range(100)],'High':[i for i in range(100)],'Low':[i for i in range(100
如果我在Pandas中有一个多索引DataFrame,如果我通读了pivot和pivot_table的文档,我似乎找不到pivot在这个例子中不能工作的原因。显然,我遗漏了一些东西,但它采用了相同的参数,似乎表明它会起作用。这里我漏掉了什么?为什么pivot抛出一个错误,而pivot_table却完美地工作。谢谢。
# standard imports
import pandas as pd
# create a random multiindex dataframe
outside = ['G1','G1','G1','G2'
我有一个熊猫DataFrame,看起来像这样: Store Month Sales
0 1 Jan 1100
1 1 Feb 1300
2 1 Mar 1250
3 1 Apr 1500
4 2 Jan 900
5 2 Feb 950
6 2 Mar 800
7 2 Apr 900 我想对它进行转换,使其看起来像: Store Jan Feb Mar
这个问题很像是Pandas pivot or reshape dataframe with NaN的后续问题 解码视频时,一些帧丢失,需要对数据进行插值 当前df frame pvol vvol area label
0 NaN 109.8 120 v
2 NaN 160.4 140 v
0 23.1 NaN 110 p
1 24.3 NaN 110 p
2 25.6 NaN 112 p 预期的df fra
我从spark数组“df_spark”开始: from pyspark.sql import SparkSession
import pandas as pd
import numpy as np
import pyspark.sql.functions as F
spark = SparkSession.builder.master("local").appName("Word Count").config("spark.some.config.option", "some-value").getOrCreate()
np