如何計算這個df Python Pandas階段之間的天數？

Question

df = pd.DataFrame({'Campaign ID':[48464,48464,48464,48464,26380,26380,22676,39529,39529,46029,46029,46029,17030,46724,46724,39379,39379,39379],
'Campaign stage':["Lost","Developing","Discussing","Starting","Discussing", "Starting","Developing",    "Discussing","Starting","Developing",   "Discussing","Starting","Developing",   "Developing","Discussing","Lost",   "Developing","Discussing"],
'Stage Number':[-1, 3,  2,  1,  2,  1,  3,  2,  1,  3,  2,  1,  3,  3,  2,  -1, 3,  2],
'Campaign Date':["2/8/2019","1/9/2019","1/3/2019","3/3/2018","2/14/2019","12/5/2018","7/25/2018","6/8/2018","3/4/2018","12/8/2018","9/9/2018","5/31/2018","6/7/2018","3/27/2018","1/6/2018","2/15/2019","12/15/2018","9/4/2018"]})

pvt = pd.pivot_table(df,values=['Campaign stage'],index=['Campaign ID','Campaign stage','Stage Number','Campaign Date'],aggfunc='count')
pvt.sort_values(['Campaign ID','Campaign Date'],ascending=[True,False])

大家好，我有上面的數據框，我想計算每個廣告系列的廣告系列“開始”和“討論”之間的天數，然后計算平均值。

由於數據質量，活動階段不一致。 因此，對於廣告系列沒有“開始”和“討論”兩個階段，我想設置為0。

我創建了數據的數據透視表視圖，並按降序對廣告系列日期進行了排序......但我不知道下一步該怎么做。

先謝謝您的幫助。

Answer 1

這是我的建議，從你的df開始：

df['Campaign Date'] = pd.to_datetime(df['Campaign Date'])
df = df[df['Campaign stage'].isin(['Starting', 'Discussing'])]

pvt = pd.pivot_table(df,values=['Campaign stage'],index=['Campaign ID','Campaign stage','Stage Number','Campaign Date'],aggfunc='count')
pvt = pvt.sort_values(['Campaign ID','Campaign Date'],ascending=True).reset_index(level=3)

pvt.groupby(level=0).diff().sum(level=0)

輸出將是：

Campaign ID - Campaign Date
26380   71 days
39379   0 days
39529   96 days
46029   101 days
46724   0 days
48464   306 days

如果您要尋找的是總平均值：

pvt.groupby(level=0).diff().sum(level=0).mean()

這是：

95 days 16:00:00

Answer 2

df['Campaign Date'] =  pd.to_datetime(df['Campaign Date'],format='%m/%d/%Y')
compare= {}
for ids,gp in df.groupby('Campaign ID'):
    try:
        compare[ids]= gp.loc[gp['Campaign stage']=='Discussing']['Campaign Date'].iloc[0] - gp.loc[gp['Campaign stage']=='Starting']['Campaign Date'].iloc[0]
    except:
        compare[ids] =0

df['new_col'] = df['Campaign ID'].apply(lambda x:compare[x])

Answer 3

我不確定您希望最終結果的形式，下面的示例假設您希望它在新的數據框中。

我們可以采用多種方式，但是，我選擇在下面創建一個函數，並為了清晰起見分解每一步。

• 第1步：將日期數據類型更新為datetime以進行比較
• 第2步：調用函數步驟2.1：定義空列表以存儲我們的cids值和starting和discussing之間的天數
  • 步驟2.2：獲取所有唯一的cid並遍歷每個cid
  • 步驟2.3：檢查cid是否同時具有我們感興趣的分段
    • 步驟2.3.1：如果沒有為此cid分配0
    • 步驟2.3.2：否則計算discussing和starting之間的差異
  • 步驟2.4：創建我們的新數據幀並返回它
  • 步驟2.5：使用df.loc創建並計算我們的平均日指數

碼

# First we need to modify your data type to compare the dates
df['Campaign Date'] = pd.to_datetime(df['Campaign Date'])

df_days = getDays(df)

# We create a new index called 'Average' that will assign values to the days column
df_days.loc['Average','days'] = df_days.days.mean()

def getDays(df):
    days = list()
    cids = list()

    for cid in df['Campaign ID'].unique():
        stages = df[(df['Campaign ID'] == cid) & 
                    (df['Campaign stage'].str.lower().isin(['discussing', 'starting']))]['Campaign stage'].unique()

        if len(stages) < 2:
            d = 0
            c = cid

            days.append(d)
            cids.append(c)

        else:
            starting = df[(df['Campaign ID'] == cid) & (df['Campaign stage'].str.lower() == 'starting')]['Campaign Date'].dt.date.values
            discussing = df[(df['Campaign ID'] == cid) & (df['Campaign stage'].str.lower() == 'discussing')]['Campaign Date'].dt.date.values

            d = (discussing - starting)[0].days

            c = cid

            days.append(d)
            cids.append(c)


    dff = pd.DataFrame({
        'cids': cids,
        'days': days
    })          

    return dff

退房

        cids    days
0       48464.0 306.00
1       26380.0 71.00
2       22676.0 0.00
3       39529.0 96.00
4       46029.0 101.00
5       17030.0 0.00
6       46724.0 0.00
7       39379.0 0.00
Average NaN   71.75