This question already has answers here:
How can I pivot a dataframe?
(5 answers)
Closed 2 years ago.
I have dataframe: table_revenue
how can I transpose the dataframe and have grouping by 'stations_id' to see final result as:
where values of cells is the price, aggregated by exact date (column) for specific 'station_id' (row)
It seems you need pivot_table():
output = input.pivot_table(index='station_id',columns='endAt',values='price',aggfunc='sum',fill_value=0)
Related
This question already has answers here:
How to combine multiple rows into a single row with pandas [duplicate]
(1 answer)
Concatenate strings from several rows using Pandas groupby
(8 answers)
Closed 1 year ago.
I have this dataframe of tweets collected with the date they were posted. I would like to know if there is any way to group these tweets by date?
For example all tweets from one day X would be on the same line in the dataframe
This question already has answers here:
How can I pivot a dataframe?
(5 answers)
How to pivot a dataframe in Pandas? [duplicate]
(2 answers)
Closed 1 year ago.
I have a dataframe like this:
index,col1,value
1,A,1
1,B,2
2,A,3
2,D,4
2,C,5
2,B,6
And I would like to convert this dataframe to this:
index,col1_A,col1_B,col1_C,col1_D
1,1,2,np.Nan,np.nan
2,3,4,5,6
The conversion is based on the index column value and for each unique index column, the column values from col1 is converted to column name and its associated value is set to the corresponding value available in value columns.
Currently my solution contains looping by creating subset of df as temporary df based on each index and then starting looping there. I am wondering if there is already builtin solution available for it in pandas. please feel free to suggest.
This question already has answers here:
How to get the last N rows of a pandas DataFrame?
(3 answers)
Closed 2 years ago.
I have the following panda dataframe:(df)
How can I append only the last row (Date 2021-01-22) to a new dataframe (df_new)?
df_new = df_new.append(df.tail(1))
if df_new is not defined. The following code will do it.
df_new = df.tail(1)
This question already has answers here:
Get statistics for each group (such as count, mean, etc) using pandas GroupBy?
(9 answers)
Closed 3 years ago.
I want to merge duplicate rows by adding a new column 'count'
Final dataframe that I want
rows can be in any order
You can use:
df["count"] = 1
df = df.groupby(["user_id", "item_id", "total"])["count"].count().reset_index()
This question already has answers here:
How to take column-slices of dataframe in pandas
(11 answers)
Closed 6 years ago.
I have a dataframe with 85 columns and something like 10.000 rows.
The first column is Shrt_Desc and the last Refuse_Pct
The new data frame that I want has to have Shrt_Desc, then leave some columns out and then include in series Fiber_TD_(g) to Refuse_Pct
I use:
dfi_3 = food_info.loc[:, ['Shrt_Desc', 'Fiber_TD_(g)':'Refuse_Pct']]
but it gives a syntax error.
Any ideas how can I achieve this?
Thank you.
Borrowing the main idea from this answer:
pd.concat([food_info['Shrt_Desc'], food_info.ix[:, 'Fiber_TD_(g)':]], axis=1)