I am trying to reshape the following dataframe such that it is in panel data form by moving the "Year" column such that each year is an individual column.
Out[34]:
Award Year 0
State
Alabama 2003 89
Alabama 2004 92
Alabama 2005 108
Alabama 2006 81
Alabama 2007 71
... ...
Wyoming 2011 4
Wyoming 2012 2
Wyoming 2013 1
Wyoming 2014 4
Wyoming 2015 3
[648 rows x 2 columns]
I want the years to each be individual columns, this is an example,
Out[48]:
State 2003 2004 2005 2006
0 NewYork 10 10 10 10
1 Alabama 15 15 15 15
2 Washington 20 20 20 20
I have read up on stack/unstack but I don't think I want a multilevel index as a result. I have been looking through the documentation at to_frame etc. but I can't see what I am looking for.
If anyone can help that would be great!
Use set_index with append=True then select the column 0 and use unstack to reshape:
df = df.set_index('Award Year', append=True)['0'].unstack()
Result:
Award Year 2003 2004 2005 2006 2007 2011 2012 2013 2014 2015
State
Alabama 89.0 92.0 108.0 81.0 71.0 NaN NaN NaN NaN NaN
Wyoming NaN NaN NaN NaN NaN 4.0 2.0 1.0 4.0 3.0
Pivot Table can help.
df2 = pd.pivot_table(df,values='0', columns='AwardYear', index=['State'])
df2
Result:
AwardYear 2003 2004 2005 2006 2007 2011 2012 2013 2014 2015
State
Alabama 89.0 92.0 108.0 81.0 71.0 NaN NaN NaN NaN NaN
Wyoming NaN NaN NaN NaN NaN 4.0 2.0 1.0 4.0 3.0
I Have a Data Frame df which is given below and I have to calculate the number of rows containing NaN values.
Name Age City Country
0 jack NaN Sydeny Australia
1 Riti NaN Delhi India
2 Vikas 31 NaN India
3 Neelu 32 Bangalore India
4 Steve 16 New York US
5 John 11 NaN NaN
6 NaN NaN NaN NaN
To get the answer I tried
df.isnull().sum().sum()
And it gives me output 9 by calculating all NaN value, but the is answer is 5 by calculating Rows which contain NaN value. I do not know how to calculate this.
You need df.any() over axis=1 after you check isnull():
df.isnull().any(axis=1).sum()
#5
Just for an example how to get it.
Example DF
>>> df
Name Age City Country
0 jack NaN Sydeny Australia
1 Riti NaN Delhi India
2 Vikas 31.0 NaN India
3 Neelu 32.0 Bangalore India
4 John 16.0 New York US
5 John 11.0 NaN NaN
6 NaN NaN NaN NaN
TO designate the Nan rows with bool...
>>> df.isnull().any(1)
0 True
1 True
2 True
3 False
4 False
5 True
6 True
dtype: bool
To get the row where Nan appeared:
>>> df.index[df.isnull().any(1)]
Int64Index([0, 1, 2, 5, 6], dtype='int64')
Last your answer directly:
>>> df.isnull().any(1).sum()
5
OR
>>> df.index[df.isnull().any(1).sum()]
5
I'm using code to shift time series data that looks somewhat similar to this:
Year Player PTSN AVGN
2018 Aaron Donald 280.60 17.538
2018 J.J. Watt 259.80 16.238
2018 Danielle Hunter 237.60 14.850
2017 Aaron Donald 181.0 12.929
2016 Danielle Hunter 204.6 12.788
with the intent of getting it into something like this:
AVGN PTSN AVGN_prev PTSN_prev
Player Year
Aaron Donald 2016 NaN NaN NaN NaN
2017 12.929 181.0 NaN NaN
2018 17.538 280.6 12.929 181.0
Danielle Hunter 2016 12.788 204.6 NaN NaN
2017 8.325 133.2 12.788 204.6
2018 14.850 237.6 8.325 133.2
J.J. Watt 2016 NaN NaN NaN NaN
2017 NaN NaN NaN NaN
2018 16.238 259.8 NaN NaN
I'm using this code to make that happen:
res = df.set_index(['player', 'Year'])
idx = pd.MultiIndex.from_product([df['player'].unique(),
df['Year'].unique()],
names=['Player', 'Year'])
res = res.groupby(['player', 'Year']).apply(sum)
res = res.reindex(idx).sort_index()
res[columns] = res.groupby('Player')[list(res.columns)].shift(1)
with the addition of a groupby.sum() because some players in the dataframe moved from one teamt o another within the same season and i want to combine those numbers. However, the data i have is actually coming out extremely wrong. The data has too many columns to post, but it seems like the data from the previous year (_prev) is placed into random columns. It doesn't change and will always place it into the same wrong columns. Is this an issue caused by the groupby.sum()? is it because i'm using a columns variable (containing all the same names as res.columns with a str(_prev) attached to them) and a list(res.columns)? And regardless of which it is, how do i solve this?
here's the outputs of columns and res.columns:
columns:
['player_id_prev', 'position_prev', 'player_game_count_prev', 'team_name_prev', 'snap_counts_total_prev', 'snap_counts_pass_rush_prev', 'snap_counts_run_defense_prev', 'snap_counts_coverage_prev', 'grades_defense_prev', 'grades_run_defense_prev', 'grades_tackle_prev', 'grades_pass_rush_defense_prev', 'grades_coverage_defense_prev', 'total_pressures_prev', 'sacks_prev', 'hits_prev', 'hurries_prev', 'batted_passes_prev', 'tackles_prev', 'assists_prev', 'missed_tackles_prev', 'stops_prev', 'forced_fumbles_prev', 'targets_prev', 'receptions_prev', 'yards_prev', 'yards_per_reception_prev', 'yards_after_catch_prev', 'longest_prev', 'touchdowns_prev', 'interceptions_prev', 'pass_break_ups_prev', 'qb_rating_against_prev', 'penalties_prev', 'declined_penalties_prev']
res_columns:
['player_id', 'position', 'player_game_count', 'team_name',
'snap_counts_total', 'snap_counts_pass_rush', 'snap_counts_run_defense',
'snap_counts_coverage', 'grades_defense', 'grades_run_defense',
'grades_tackle', 'grades_pass_rush_defense', 'grades_coverage_defense',
'total_pressures', 'sacks', 'hits', 'hurries', 'batted_passes',
'tackles', 'assists', 'missed_tackles', 'stops', 'forced_fumbles',
'targets', 'receptions', 'yards', 'yards_per_reception',
'yards_after_catch', 'longest', 'touchdowns', 'interceptions',
'pass_break_ups', 'qb_rating_against', 'penalties',
'declined_penalties']
both are length 35 when tested.
I suggest use:
#first aggregate for unique MultiIndex
res = df.groupby(['Player', 'Year']).sum()
#MultiIndex
idx = pd.MultiIndex.from_product(res.index.levels,
names=['Player', 'Year'])
#aded new missing years
res = res.reindex(idx).sort_index()
#shift all columns, add suffix and join to original
res = res.join(res.groupby('Player').shift().add_suffix('_prev'))
print (res)
PTSN AVGN PTSN_prev AVGN_prev
Player Year
Aaron Donald 2016 NaN NaN NaN NaN
2017 181.0 12.929 NaN NaN
2018 280.6 17.538 181.0 12.929
Danielle Hunter 2016 204.6 12.788 NaN NaN
2017 NaN NaN 204.6 12.788
2018 237.6 14.850 NaN NaN
J.J. Watt 2016 NaN NaN NaN NaN
2017 NaN NaN NaN NaN
2018 259.8 16.238 NaN NaN
I have a dataset that has been merged together to fill missing values from one another.
The problem is that I have some columns with missing data that I want to now fill with the values that aren't missing.
The merged data set looks like this for an input:
Name State ID Number_x Number_y Op_x Op_y
Johnson AL 1 1 nan 1956 nan
Johnson AL 1 nan nan 1956 nan
Johnson AL 2 1 nan 1999 nan
Johnson AL 2 0 nan 1999 nan
Debra AK 1A 0 nan 2000 nan
Debra AK 1B nan 20 nan 1997
Debra AK 2 nan 10 nan 2009
Debra AK 3 nan 1 nan 2008
.
.
What I'd want for an output is this:
Name State ID Number_x Number_y Op_x Op_y
Johnson AL 1 1 1 1956 1956
Johnson AL 2 1 1 1999 1999
Johnson AL 2 0 0 1999 1999
Debra AK 1A 0 0 2000 2000
Debra AK 1B 20 20 1997 1997
Debra AK 2 10 10 2009 2009
Debra AK 3 1 1 2008 2008
.
.
So I want it so that all nan values are replaced by the associated values in their columns - match Number_x to Number_y and Op_x to Op_y.
One thing to note is that when there are two IDs that are the same sometimes their values will be different; like Johnson with ID = 2 which has different numbers but the same op values. I want to keep these because I need to investigate them more.
Also, if the row has two missing values for Number_x and Number_y I want to take that row out - like Johnson with Number_x and Number_y missing as a nan value.
let us do groupby with axis =1
df.groupby(df.columns.str.split('_').str[0],1).first().dropna(subset=['Number','Op'])
ID Name Number Op State
0 1 Johnson 1.0 1956.0 AL
2 2 Johnson 1.0 1999.0 AL
3 2 Johnson 0.0 1999.0 AL
4 1A Debra 0.0 2000.0 AK
5 1B Debra 20.0 1997.0 AK
6 2 Debra 10.0 2009.0 AK
7 3 Debra 1.0 2008.0 AK
I have the following DataFrame. Describe the cities where each user lived
City Name Date
0 Seattle Alice 2017
1 Seattle Bob 2011
2 Portland Mallory 2010
3 Seattle Mallory 2016
4 Memphis Bob 2012
5 Portland Mallory 2013
Can you with pandas achieve the following?
Name City1 Date1 City2 Date2 City3 Date3
0 Alice Seattle 2017 NaN NaN NaN NaN
1 Bob Seattle 2011 Memphis 2012 NaN NaN
2 Mallory Portland 2010 Seattle 2016 Portland 2013
Thank you very much!
You can use groupby with custom function where create new DataFrame, then unstack, sort second level of MultiIndex by sort_index and last use join for remove it:
df1 = df.groupby('Name')['City','Date']
.apply(lambda x: pd.DataFrame(x.values,
columns = ['City','Date'],
index = np.arange(1, len(x) + 1).astype(str)))
.unstack()
df1 = df1.sort_index(axis=1, level=1).replace({None:np.nan})
df1.columns = df1.columns.map(''.join)
print (df1)
City1 Date1 City2 Date2 City3 Date3
Name
Alice Seattle 2017 NaN NaN NaN NaN
Bob Seattle 2011 Memphis 2012.0 NaN NaN
Mallory Portland 2010 Seattle 2016.0 Portland 2013.0