这是当前表格的示例：
game_id |player_id|player_name |event_id|event_desc |count |
--------|---------|------------------|--------|------------------|-------|
1 | 1|player1 | 3|Shot | 1|
1 | 1|player1 | 5|Rebound | 3|
1 | 1|player1 | 7|Foul | 1|
1 | 1|player1 | 14|Assist | 1|
1 | 1|player1 | 17|Subbed in | 4|
1 | 1|player1 | 18|Subbed out | 3|
1 | 1|player1 | 19|Drew a Foul | 2|
1 | 1|player1 | 20|Free Throws Scored| 3|
1 | 1|player1 | 21|Free Throws Missed| 1|
1 | 2|player2 | 3|Shot | 7|
1 | 2|player2 | 4|Miss | 10|
1 | 2|player2 | 5|Rebound | 2|
1 | 2|player2 | 7|Foul | 1|
1 | 2|player2 | 14|Assist | 1|
1 | 2|player2 | 17|Subbed in | 4|
1 | 2|player2 | 18|Subbed out | 4|
1 | 2|player2 | 19|Drew a Foul | 2|
我需要基于此创建一个视图，以便将每个玩家的每场比赛统计信息分组。不同的统计信息基于该特定event_ID的计数。我大约有20个不同的ID可以映射。
game id | player_id | shot | Miss | Rebound |Foul | Assist | ...
1 |1 |1 |0 |3 |1 |1
1 |2 |7 |10 |2 |1 |1
我认为我必须为此使用交叉表功能，但是我不确定如何准确地为此编写代码，我并不是一个真正了解此领域的人。如果有人可以帮助我，我将不胜感激。
尝试以下代码：
select
game_id,
player_id,
player_name,
sum(case when event_desc = 'Jump Ball' then wjxbfs1 else 0) as Jump_Ball,
sum(case when event_desc = 'Shot' then wjxbfs1 else 0) as Shot,
sum(case when event_desc = 'Miss' then wjxbfs1 else 0) as Miss,
sum(case when event_desc = 'Rebound' then wjxbfs1 else 0) as Rebound,
sum(case when event_desc = 'Assist' then wjxbfs1 else 0) as Assist,
sum(case when event_desc = 'Block' then wjxbfs1 else 0) as Block,
sum(case when event_desc = 'Steal' then wjxbfs1 else 0) as Steal,
sum(case when event_desc = 'Turnover' then wjxbfs1 else 0) as Turnover,
sum(case when event_desc = 'Foul' then wjxbfs1 else 0) as Foul,
sum(case when event_desc = 'Free Throws Taken' then wjxbfs1 else 0) as FT_Taken,
sum(case when event_desc = 'Free Throws Scored' then wjxbfs1 else 0) as FT_Scored,
sum(case when event_desc = 'Free Throws MIssed' then wjxbfs1 else 0) as FT_Missed,
sum(case when event_desc = 'Timeout' then wjxbfs1 else 0) as Timeout,
sum(case when event_desc = 'Violation' then wjxbfs1 else 0) as Violation,
sum(case when event_desc = 'Subbed in' then wjxbfs1 else 0) as Subbed_In,
sum(case when event_desc = 'Subbed out' then wjxbfs1 else 0) as Subbed_Out,
sum(case when event_desc = 'Drew a Foul' then wjxbfs1 else 0) as Drew_Foul,
sum(case when event_desc = 'Ejection' then wjxbfs1 else 0) as Ejected
from
stats
group by
game_id,
player_id,
player_name
返回以下错误： SQL错误[42601]：错误：“）”或附近的语法错误位置：119
此问题已解决，方法是将“ else 0”替换为“ end”。
答案 0 :(得分：1)
您可以尝试使用以下情况下的情况
jcr:title.de答案 1 :(得分：1)
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import datetime as dt
datenmodelo = pd.read_csv('1163655_010319_OZ_ALL_2.csv', sep=';', usecols=['Monat','Datum', 'Zeit', 'T_oM', 'RH_oM', 'T_uM', 'RH_uM', 'p_M', 'G_M', 'PAR_M'])
daten_si_M = datenmodelo.set_index(['Datum'])
dfmt1 = mdates.DateFormatter('%H:%M')
x_date_M = [dt.datetime.strptime(d,'%d.%m.%Y').date() for d in dates_M]
a = 0
start = 0
end = 1
for daysM in dates_M[start:end]:
# Definition aller Funktionswerte/Größen (eingeschlossen NaN)
T_uMd= pd.to_numeric(daten_si_M.loc[daysM].T_uM, errors='coerce')
T_oMd = pd.to_numeric(daten_si_M.loc[daysM].T_oM, errors='coerce')
RH_uMd = pd.to_numeric(daten_si_M.loc[daysM].RH_uM, errors='coerce')
RH_oMd = pd.to_numeric(daten_si_M.loc[daysM].RH_oM, errors='coerce')
G_Md = pd.to_numeric(daten_si_M.loc[daysM].G_M, errors='coerce')
p_Md = pd.to_numeric(p_M_korr.loc[daysM], errors='coerce')
PAR_Md= pd.to_numeric(daten_si_M.loc[daysM].PAR_M, errors='coerce')
x_time_M = pd.to_datetime(daten_si_M.loc[daysM].Zeit)
f, axarr = plt.subplots(4,1)
f.set_size_inches(15, 20)
# Titel der Graphen und Achsen:
axarr[0].set_title('Modelo - %s'%daysM, fontsize=14, fontweight='bold')
axarr[3].set_xlabel('Uhrzeit', fontweight='bold')
axarr[1].set_ylabel('T [°C]', fontweight='bold')
axarr[2].set_ylabel('RH [%]', fontweight='bold')
axarr[0].set_ylabel('G [W/m^2]', fontweight='bold')
axarr[3].set_ylabel('p [hPa]', fontweight='bold')
# Formatierung der Achsen:
axarr[0].xaxis.set_major_formatter(dfmt1)
axarr[1].xaxis.set_major_formatter(dfmt1)
axarr[2].xaxis.set_major_formatter(dfmt1)
axarr[3].xaxis.set_major_formatter(dfmt1)
# Plot der Variablen und Label der Kurve:
axarr[1].plot(x_time_M, T_uMd,'r', label='Temperatur (unten)')
axarr[1].plot(x_time_M, T_oMd, color='indigo', label='Temperatur (oben)')
axarr[2].plot(x_time_M, RH_uMd,'r', label='Relative Feuchte (unten)')
axarr[2].plot(x_time_M, RH_oMd,color='indigo', label='Relative Feuchte (oben)')
axarr[0].plot(x_time_M, G_Md,'b', label='Globalstrahlung')
axarr[0].plot(x_time_M, PAR_Md,'c', label='Photosynthetische Strahlung')
axarr[3].plot(x_time_M, p_Md,'g', label='Druck')
# Positionierung der Labels:
axarr[0].legend(loc='center left', bbox_to_anchor=(1.05, 0.5))
axarr[1].legend(loc='center left', bbox_to_anchor=(1.05, 0.5))
axarr[2].legend(loc='center left', bbox_to_anchor=(1.05, 0.5))
axarr[3].legend(loc='center left', bbox_to_anchor=(1.05, 0.5))
# Gitter:
plt.rc('grid', linestyle="dashed", color='b', alpha=0.5, linewidth=0.5)
plt.rcParams['axes.grid'] = True
# Plot:
plt.savefig('Modelo (GTRHp) - %s.png' %(str(daysM)), bbox_inches='tight', dpi=300)
plt.close()
a += 1
plt.show()
是很好的语法。但是，在Postgres中，我很喜欢max(case when . . . end)关键字。这也可以表示为：
filter请注意，这稍快一些，语法是ISO / ANSI标准（尽管我认为其他任何数据库都没有）。
答案 2 :(得分：0)
我不确定postgresql是否像SQLServer一样支持数据透视。但是总的来说，我使用case语句来透视数据。
select
game_id,
player_id,
player_name,
sum(case when event_desc = 'shot' then count else 0 end) as shot,
sum(case when event_desc = 'Miss' then count else 0 end) as Miss,
-- and so forth for every single event_desc you want to pivot as a column
from
current_table
group by
game_id,
player_id,
player_name
那应该为您解决问题。