python-pandas-检查数据框中是否存在日期
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python - pandas - check if date exists in dataframe
提问by Leo
I have a dataframe like this:
我有一个这样的数据框：
category date number
0 Cat1 2010-03-01 1
1 Cat2 2010-09-01 1
2 Cat3 2010-10-01 1
3 Cat4 2010-12-01 1
4 Cat5 2012-04-01 1
5 Cat2 2013-02-01 1
6 Cat3 2013-07-01 1
7 Cat4 2013-11-01 2
8 Cat5 2014-11-01 5
9 Cat2 2015-01-01 1
10 Cat3 2015-03-01 1
I would like to check if a date is exist in this dataframe but I am unable to. I tried various ways as below but still no use:
我想检查此数据框中是否存在日期，但我无法检查。我尝试了以下各种方法，但仍然没有用：
if pandas.Timestamp("2010-03-01 00:00:00", tz=None) in df['date'].values:
print 'date exist'
if datetime.strptime('2010-03-01', '%Y-%m-%d') in df['date'].values:
print 'date exist'
if '2010-03-01' in df['date'].values:
print 'date exist'
The 'date exist' never got printed. How could I check if the date exist? Because I want to insert the none-existed date with number equals 0 to all the categories so that I could plot a continuously line chart (one category per line). Help is appreciated. Thanks in advance.
“存在日期”从未被打印出来。我如何检查日期是否存在？因为我想在所有类别中插入数字等于 0 的不存在日期，以便我可以绘制连续折线图（每行一个类别）。帮助表示赞赏。提前致谢。
The last one gives me this:
FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparisonAnd the date existnot get printed.
最后一个给了我这个：
FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison而date exist不是被打印出来。
采纳答案by jezrael
I think you need convert to datetime first by to_datetimeand then if need select all rows use boolean indexing:
我认为您需要先转换为日期时间to_datetime，然后如果需要选择所有行，请使用boolean indexing：
df.date = pd.to_datetime(df.date)
print (df.date == pd.Timestamp("2010-03-01 00:00:00"))
0 True
1 False
2 False
3 False
4 False
5 False
6 False
7 False
8 False
9 False
10 False
Name: date, dtype: bool
print (df[df.date == pd.Timestamp("2010-03-01 00:00:00")])
category date number
0 Cat1 2010-03-01 1
For return Trueuse check value converted to numpy arrayby values:
对于返回True使用校验值转换为numpy array通过values：
if ('2010-03-01' in df['date'].values):
print ('date exist')
Or at least one Trueby anyas comment Edchum:
if (df.date == pd.Timestamp("2010-03-01 00:00:00")).any():
print ('date exist')
回答by JoseGzz
For example, to cofirm that the 4th value of dsis contained within itself:
例如，要确认 的第 4 个值ds包含在其自身中：
len(set(ds.isin([ds.iloc[3]]))) > 1
Let dsbe a Pandas DataSeries of the form [index, pandas._libs.tslib.Timestamp] with example values:
让我们ds成为一个带有示例值的 [index, pandas._libs.tslib.Timestamp] 形式的 Pandas DataSeries：
0 2018-01-31 19:08:27.465515
1 2018-02-01 19:08:27.465515
2 2018-02-02 19:08:27.465515
3 2018-02-03 19:08:27.465515
4 2018-02-04 19:08:27.465515
0 2018-01-31 19:08:27.465515
1 2018-02-01 19:08:27.465515
2 2018-02-02 19:08:27.465515
3 2018-02-03 19:08:27.465515
4 2018-02-04 19:08:27.465515
Then, we use the isinlocal method to get a DataSeries of booleans where each entry indicates wether that position in dsmatches with the value passed as argument to the function (since isinexpects a list of values we need to provide the value in list format).
然后，我们使用isin本地方法获取布尔值的 DataSeries，其中每个条目指示该位置是否ds与作为参数传递给函数的值匹配（因为isin需要一个值列表，我们需要以列表格式提供值）。
Next, we use the setglobal method as to get a set with 1 or 2 values depending on wether there was a match (True and False values) or not (only a False value).
接下来，我们使用set全局方法获取具有 1 或 2 个值的集合，具体取决于是否存在匹配（True 和 False 值）或不匹配（只有 False 值）。
Finally, we check if the set contains more than 1 value, if that is the case, it means we have a match, and no match otherwise.
最后，我们检查集合是否包含超过 1 个值，如果是这样，则表示我们有匹配项，否则就没有匹配项。
