使用 DateTimeIndex 计算 Dataframe 中字符串的出现次数

Question

我有一个具有如下时间序列的 DataFrame：

timestamp   v            IceCreamOrder  Location
2018-01-03  02:21:16     Chocolate      South
2018-01-03  12:41:12     Vanilla        North
2018-01-03  14:32:15     Strawberry     North
2018-01-03  15:32:15     Strawberry     North
2018-01-04  02:21:16     Strawberry     North
2018-01-04  02:21:16     Rasberry       North
2018-01-04  12:41:12     Vanilla        North
2018-01-05  15:32:15     Chocolate      North

我想得到这样的计数：

timestamp   strawberry  chocolate
1/2/14      0           1
1/3/14      2           0
1/4/14      1           0
1/4/14      0           0
1/4/14      0           0
1/5/14      0           1

由于这是时间序列数据，我一直以 pandas datetimeindex 格式存储时间戳。

我首先尝试获取 'strawberry' 的计数。我最终得到了这段不起作用的代码。

mydf = (inputdf.set_index('timestamp').groupby(pd.Grouper(freq = 'D'))['IceCreamOrder'].count('Strawberry'))

导致错误：

TypeError: count() takes 1 positional argument but 2 were given

如有任何帮助，我们将不胜感激。

Answer 1

使用 eq (==) 比较列 string 并聚合 sum 计数 True 值，因为 Trues是像 1s:

这样的进程

#convert to datetimes if necessary
inputdf['timestamp'] = pd.to_datetime(inputdf['timestamp'], format='%m/%d/%y')
print (inputdf)
   timestamp IceCreamOrder Location
0 2018-01-02     Chocolate    South
1 2018-01-03       Vanilla    North
2 2018-01-03    Strawberry    North
3 2018-01-03    Strawberry    North
4 2018-01-04    Strawberry    North
5 2018-01-04      Rasberry    North
6 2018-01-04       Vanilla    North
7 2018-01-05     Chocolate    North

mydf = (inputdf.set_index('timestamp')['IceCreamOrder']
               .eq('Strawberry')
               .groupby(pd.Grouper(freq = 'D'))
               .sum())
print (mydf)
timestamp
2018-01-02    0.0
2018-01-03    2.0
2018-01-04    1.0
2018-01-05    0.0
Freq: D, Name: IceCreamOrder, dtype: float64

如果要计算所有 type，请将列 IceCreamOrder 添加到 groupby 并汇总 GroupBy.size:

mydf1 = (inputdf.set_index('timestamp')
               .groupby([pd.Grouper(freq = 'D'), 'IceCreamOrder'])
               .size())
print (mydf1)
timestamp   IceCreamOrder
2018-01-02  Chocolate        1
2018-01-03  Strawberry       2
            Vanilla          1
2018-01-04  Rasberry         1
            Strawberry       1
            Vanilla          1
2018-01-05  Chocolate        1
dtype: int64

mydf1 = (inputdf.set_index('timestamp')
               .groupby([pd.Grouper(freq = 'D'),'IceCreamOrder'])
               .size()
               .unstack(fill_value=0))
print (mydf1)
IceCreamOrder  Chocolate  Rasberry  Strawberry  Vanilla
timestamp                                              
2018-01-02             1         0           0        0
2018-01-03             0         0           2        1
2018-01-04             0         1           1        1
2018-01-05             1         0           0        0

如果所有datetime都没有time：

mydf1 = (inputdf.groupby(['timestamp', 'IceCreamOrder'])
                .size()
                .unstack(fill_value=0))
print (mydf1)
IceCreamOrder  Chocolate  Rasberry  Strawberry  Vanilla
timestamp                                              
2018-01-02             1         0           0        0
2018-01-03             0         0           2        1
2018-01-04             0         1           1        1
2018-01-05             1         0           0        0

Answer 2

使用pivot_table:

df.pivot_table(
    index='timestamp', columns='IceCreamOrder', aggfunc='size'
).fillna(0).astype(int)

IceCreamOrder  Chocolate  Rasberry  Strawberry  Vanilla
timestamp
2018-01-02             1         0           0        0
2018-01-03             0         0           2        1
2018-01-04             0         1           1        1
2018-01-05             1         0           0        0

或crosstab:

pd.crosstab(df.timestamp, df.IceCreamOrder)

IceCreamOrder  Chocolate  Rasberry  Strawberry  Vanilla
timestamp
2018-01-02             1         0           0        0
2018-01-03             0         0           2        1
2018-01-04             0         1           1        1
2018-01-05             1         0           0        0

如果您的 timestamp 列有时间，只需在使用 dt.date 使用这些操作之前删除它们（如果您不想修改该列，也许可以创建一个新系列用于旋转):

df.timestamp = df.timestamp.dt.date

使用 DateTimeIndex 计算 Dataframe 中字符串的出现次数

Count occurrences of a string in a Dataframe with a DateTimeIndex

python

dataframe

pandas

datetimeindex