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Assign Name to Derived Column in Python
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Data science time! April 2019 and salary with experience
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I am new to Python. I have read two columns say nTime and nMessageId from a table using read_sql function and stored it in variable. for e.g.
df = pd.read_sql(query)
Result for this is:
nTime nMessageId
22/03/2019 08:31:00 2552
22/03/2019 08:31:00 1025
22/03/2019 08:31:00 2552
22/03/2019 08:31:00 1342
22/03/2019 08:31:01 1342
22/03/2019 08:31:02 128
Then further I have derived top 10 records based on the count of the records on column nTime using value_counts(ascending = False).head(10) function. i.e.
df2 = df['nTime'].value_counts(ascending = False).head(10)
Then the output may be
22/03/2019 08:31:00 882
22/03/2019 08:31:01 342
22/03/2019 08:31:02 128
Now I want to do inner join of df['nTime'] and df2 and derive count of each nMessage i.e. at 22/03/2019 08:31:00 for nMessageId= 2552 the count of records will be 2.
How do I achieve the above using merge function or any other way as I am not getting column name in df2?
Please help.
python pandas dataframe merge
edited Mar 22 at 15:42
TrebledJ
4,04821432
asked Mar 22 at 15:06
SaumikSaumik
61
add a comment |
I am new to Python. I have read two columns say nTime and nMessageId from a table using read_sql function and stored it in variable. for e.g.
df = pd.read_sql(query)
Result for this is:
nTime nMessageId
22/03/2019 08:31:00 2552
22/03/2019 08:31:00 1025
22/03/2019 08:31:00 2552
22/03/2019 08:31:00 1342
22/03/2019 08:31:01 1342
22/03/2019 08:31:02 128
Then further I have derived top 10 records based on the count of the records on column nTime using value_counts(ascending = False).head(10) function. i.e.
df2 = df['nTime'].value_counts(ascending = False).head(10)
Then the output may be
22/03/2019 08:31:00 882
22/03/2019 08:31:01 342
22/03/2019 08:31:02 128
Now I want to do inner join of df['nTime'] and df2 and derive count of each nMessage i.e. at 22/03/2019 08:31:00 for nMessageId= 2552 the count of records will be 2.
How do I achieve the above using merge function or any other way as I am not getting column name in df2?
Please help.
python pandas dataframe merge
edited Mar 22 at 15:42
TrebledJ
4,04821432
asked Mar 22 at 15:06
SaumikSaumik
61
df["count"] = df.groupby(["nMessageId", "nTime"]).transform("size")
– PMende
Mar 22 at 17:28
add a comment |
I am new to Python. I have read two columns say nTime and nMessageId from a table using read_sql function and stored it in variable. for e.g.
df = pd.read_sql(query)
Result for this is:
nTime nMessageId
22/03/2019 08:31:00 2552
22/03/2019 08:31:00 1025
22/03/2019 08:31:00 2552
22/03/2019 08:31:00 1342
22/03/2019 08:31:01 1342
22/03/2019 08:31:02 128
Then further I have derived top 10 records based on the count of the records on column nTime using value_counts(ascending = False).head(10) function. i.e.
df2 = df['nTime'].value_counts(ascending = False).head(10)
Then the output may be
22/03/2019 08:31:00 882
22/03/2019 08:31:01 342
22/03/2019 08:31:02 128
Now I want to do inner join of df['nTime'] and df2 and derive count of each nMessage i.e. at 22/03/2019 08:31:00 for nMessageId= 2552 the count of records will be 2.
How do I achieve the above using merge function or any other way as I am not getting column name in df2?
Please help.
python pandas dataframe merge
edited Mar 22 at 15:42
TrebledJ
4,04821432
asked Mar 22 at 15:06
SaumikSaumik
61
I am new to Python. I have read two columns say nTime and nMessageId from a table using read_sql function and stored it in variable. for e.g.
df = pd.read_sql(query)
Result for this is:
nTime nMessageId
22/03/2019 08:31:00 2552
22/03/2019 08:31:00 1025
22/03/2019 08:31:00 2552
22/03/2019 08:31:00 1342
22/03/2019 08:31:01 1342
22/03/2019 08:31:02 128
Then further I have derived top 10 records based on the count of the records on column nTime using value_counts(ascending = False).head(10) function. i.e.
df2 = df['nTime'].value_counts(ascending = False).head(10)
Then the output may be
22/03/2019 08:31:00 882
22/03/2019 08:31:01 342
22/03/2019 08:31:02 128
Now I want to do inner join of df['nTime'] and df2 and derive count of each nMessage i.e. at 22/03/2019 08:31:00 for nMessageId= 2552 the count of records will be 2.
How do I achieve the above using merge function or any other way as I am not getting column name in df2?
Please help.
python pandas dataframe merge
python pandas dataframe merge
edited Mar 22 at 15:42
TrebledJ
4,04821432
asked Mar 22 at 15:06
SaumikSaumik
61
edited Mar 22 at 15:42
TrebledJ
4,04821432
asked Mar 22 at 15:06
SaumikSaumik
61
edited Mar 22 at 15:42
TrebledJ
4,04821432
edited Mar 22 at 15:42
TrebledJ
4,04821432
edited Mar 22 at 15:42
TrebledJ
4,04821432
4,04821432
asked Mar 22 at 15:06
SaumikSaumik
61
asked Mar 22 at 15:06
SaumikSaumik
61
asked Mar 22 at 15:06
SaumikSaumik
61
61
df["count"] = df.groupby(["nMessageId", "nTime"]).transform("size")
– PMende
Mar 22 at 17:28
add a comment |
df["count"] = df.groupby(["nMessageId", "nTime"]).transform("size")
– PMende
Mar 22 at 17:28
df["count"] = df.groupby(["nMessageId", "nTime"]).transform("size")– PMende
Mar 22 at 17:28
df["count"] = df.groupby(["nMessageId", "nTime"]).transform("size")– PMende
Mar 22 at 17:28
add a comment |
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df["count"] = df.groupby(["nMessageId", "nTime"]).transform("size")– PMende
Mar 22 at 17:28