ID Sequence float Freq Count
3631 D 1.31 1 1
P 1.45 1 1
R 1.44 1 1
3633 D 1.26 3 3
1.27 2 2
1.32 1 1
P 1.33 4 4
the above is the output of pandas groupby
final_df = small_df.groupby(['ID','Seq','float'])['ID','Seq'].count()
I would like to write this to a csv file as
3631,"D,P,R","1.31,1.45,1.44"
3633,"D,P","1.26,1.27,1.32,1.33"
would like some help in this research work.
thank you
The essence of this problem is just a grouping operation on the ID, followed by an aggregation with str.join.
df.reset_index(level=1)\ # reset the first level
.iloc[:, :2]\ # select only the first 2 columns
.astype(str)\ # convert to string
.groupby(level=0)\ # group by the index
.agg(','.join)\ # join elements
.to_csv('file.csv', quotechar='"') # save to CSV with a quoting character
file.csv
ID,Sequence,float
3631,"D,P,R","1.31,1.45,1.44"
3633,"D,D,D,P","1.26,1.27,1.32,1.33"
Related
I have a pandas dataframe with some very extreme value - more than 5 std.
I want to replace, per column, each value that is more than 5 std with the max other value.
For example,
df = A B
1 2
1 6
2 8
1 115
191 1
Will become:
df = A B
1 2
1 6
2 8
1 8
2 1
What is the best way to do it without a for loop over the columns?
s=df.mask((df-df.apply(lambda x: x.std() )).gt(5))#mask where condition applies
s=s.assign(A=s.A.fillna(s.A.max()),B=s.B.fillna(s.B.max())).sort_index(axis = 0)#fill with max per column and resort frame
A B
0 1.0 2.0
1 1.0 6.0
2 2.0 8.0
3 1.0 8.0
4 2.0 1.0
Per the discussion in the comments you need to decide what your threshold is. say it is q=100, then you can do
q = 100
df.loc[df['A'] > q,'A'] = max(df.loc[df['A'] < q,'A'] )
df
this fixes column A:
A B
0 1 2
1 1 6
2 2 8
3 1 115
4 2 1
do the same for B
Calculate a column-wise z-score (if you deem something an outlier if it lies outside a given number of standard deviations of the column) and then calculate a boolean mask of values outside your desired range
def calc_zscore(col):
return (col - col.mean()) / col.std()
zscores = df.apply(calc_zscore, axis=0)
outlier_mask = zscores > 5
After that it's up to you to fill the values marked with the boolean mask.
df[outlier_mask] = something
I have a dictionary which contains a id and a list of corresponding values for that id.
I am attempting to map this dictionary to a pandas df.
The df contains the same id to map to, but it needs to map the items in that list in order of appearance within the df.
For example:
sample_dict = {0:[0.1,0.4,0.5], 1:[0.2,0.14,0.3], 2:[0.2,0.1,0.4]}
The df looks like:
The output of mapping the dictionary to the df would look like:
Sorry for typing the table out like this, the actual df is very large, and I'm still new to stack exchange and pandas.
The end output should just map the id list value in order to the players as they appear in order as the df is sorted by id and then player
Let us try explode with reindex
df['new'] = pd.Series(sample_dict).reindex(df.id.unique()).explode().values
df
Out[140]:
id Player new
0 0 1 0.1
1 0 2 0.4
2 0 3 0.5
3 1 1 0.2
4 1 2 0.14
5 1 3 0.3
6 2 1 0.2
7 2 2 0.1
8 2 3 0.4
Here is a code sniplet to simulate the problem i am facing. i am using iteration on large datasets
df = pd.DataFrame({'grp':np.random.choice([1,2,3,4,5],500),'col1':np.arange(0,500),'col2':np.random.randint(0,10,500),'col3':np.nan})
for index, row in df.iterrows():
#based on group label, get last 3 values to calculate mean
d=df.iloc[0:index].groupby('grp')
try:
dgrp_sum=d.get_group(row.grp).col2.tail(3).mean()
except:
dgrp_sum=999
#after getting last 3 values of group with reference to current row reference, multiply by other rows
df.at[index,'col3']=dgrp_sum*row.col1*row.col2
if i want to speed it up with vectors, how do i convert this code?
You basically calculate moving average over every group.
Which means you can group dataframe by "grp" and calculate rolling mean.
At the end you multiply columns in each row because it is not dependent on group.
df["col3"] = df.groupby("grp").col2.rolling(3, min_periods=1).mean().reset_index(0,drop=True)
df["col3"] = df[["col1", "col2", "col3"]].product(axis=1)
Note: In your code, each calculated mean is placed in the next row, thats why you probably have this try block.
# Skipping last product gives only mean
# np.random.seed(1234)
# print(df[df["grp"] == 2])
grp col1 col2 iter mask
4 2 4 6 999.000000 6.000000
5 2 5 0 6.000000 3.000000
6 2 6 9 3.000000 5.000000
17 2 17 1 5.000000 3.333333
27 2 27 9 3.333333 6.333333
I have a dataframe
A B C
1 2 3
2 3 4
3 8 7
I want to take only rows where there is a sequence of 3,4 in columns C (in this scenario - first two rows)
What will be the best way to do so?
You can use rolling for general solution working with any pattern:
pat = np.asarray([3,4])
N = len(pat)
mask= (df['C'].rolling(window=N , min_periods=N)
.apply(lambda x: (x==pat).all(), raw=True)
.mask(lambda x: x == 0)
.bfill(limit=N-1)
.fillna(0)
.astype(bool))
df = df[mask]
print (df)
A B C
0 1 2 3
1 2 3 4
Explanation:
use rolling.apply and test pattern
replace 0s to NaNs by mask
use bfill with limit for filling first NANs values by last previous one
fillna NaNs to 0
last cast to bool by astype
Use shift
In [1085]: s = df.eq(3).any(1) & df.shift(-1).eq(4).any(1)
In [1086]: df[s | s.shift()]
Out[1086]:
A B C
0 1 2 3
1 2 3 4
I would like to convert the following dataframe into a json .
df:
A sector B sector C sector
TTM Ratio -- 35.99 12.70 20.63 14.75 23.06
RRM Sales -- 114.57 1.51 5.02 1.00 4594.13
MQR book 1.48 2.64 1.02 2.46 2.73 2.74
TTR cash -- 14.33 7.41 15.35 8.59 513854.86
In order to do so by using the function df.to_json() I would need to have unique names in column and indices.
Therefore what I am looking for is to convert the column names into a row and have default column numbers . In short I would like the following output:
df:
0 1 2 3 4 5
A sector B sector C sector
TTM Ratio -- 35.99 12.70 20.63 14.75 23.06
RRM Sales -- 114.57 1.51 5.02 1.00 4594.13
MQR book 1.48 2.64 1.02 2.46 2.73 2.74
TTR cash -- 14.33 7.41 15.35 8.59 513854.86
Turning the column names into the first row so I can make the conversion correctly .
You could also use vstack in numpy:
>>> df
x y z
0 8 7 6
1 6 5 4
>>> pd.DataFrame(np.vstack([df.columns, df]))
0 1 2
0 x y z
1 8 7 6
2 6 5 4
The columns become the actual first row in this case.
Use assign by list of range and original column names:
print (range(len(df.columns)))
range(0, 6)
#for python2 list can be omit
df.columns = [list(range(len(df.columns))), df.columns]
Or MultiIndex.from_arrays:
df.columns = pd.MultiIndex.from_arrays([range(len(df.columns)), df.columns])
Also is possible use RangeIndex:
print (pd.RangeIndex(len(df.columns)))
RangeIndex(start=0, stop=6, step=1)
df.columns = pd.MultiIndex.from_arrays([pd.RangeIndex(len(df.columns)), df.columns])
print (df)
0 1 2 3 4 5
A sector B sector C sector
TTM Ratio -- 35.99 12.70 20.63 14.75 23.06
RRM Sales -- 114.57 1.51 5.02 1.00 4594.13
MQR book 1.48 2.64 1.02 2.46 2.73 2.74
TTR cash -- 14.33 7.41 15.35 8.59 513854.86