python-旋转熊猫数据框以生成(季节性)热图

我是Python新手,还是SO新手.

我有一个名为df的熊猫数据框,看起来像：

                                 Text
Date        Location                           
2015-07-08  San Diego, CA        1
2015-07-07  Bellevue, WA         1
            Los Angeles, CA      1
            New York, NY         1
            Los Angeles, CA      1
            UnkNown              1

我想使用以下数据透视表：

import pandas, numpy as np

df_piVoted = df.pivot_table(df, values=['Text'], index=['Date'],
    columns=['Location'],aggfunc=np.sum)

想法是生成一个热图,以按“位置”和“日期”显示“文本”的计数.

我得到错误：

TypeError: pivot_table() got multiple values for keyword argument 'values'

使用简化方法时：

df = df.pivot_table('Date', 'Location', 'Text')

我得到错误：

raise DataError('No numeric types to aggregate')

我正在使用Python 2.7和Pandas 0.16.2

In[2]: df.dtypes
Out[2]: 
Date        datetime64[ns]
Text                object
Location            object
dtype: object

有人有主意吗？

解决方法:

import pandas as pd
import numpy as np

# just try to replicate your dataframe
# ==============================================
date = ['2015-07-08', '2015-07-07', '2015-07-07', '2015-07-07', '2015-07-07', '2015-07-07']
location = ['San Diego, CA', 'Bellevue, WA', 'Los Angeles, CA', 'New York, NY', 'Los Angeles, CA', 'UnkNown']
text = [1] * 6
df = pd.DataFrame({'Date': date, 'Location': location, 'Text': text})

Out[141]: 
         Date         Location  Text
0  2015-07-08    San Diego, CA     1
1  2015-07-07     Bellevue, WA     1
2  2015-07-07  Los Angeles, CA     1
3  2015-07-07     New York, NY     1
4  2015-07-07  Los Angeles, CA     1
5  2015-07-07          UnkNown     1

# processing
# ==============================================
pd.pivot_table(df, index='Date', columns='Location', values='Text', aggfunc=np.sum)

Out[142]: 
Location    Bellevue, WA  Los Angeles, CA  New York, NY  San Diego, CA  UnkNown
Date                                                                           
2015-07-07             1                2             1            NaN        1
2015-07-08           NaN              NaN           NaN              1      NaN