Input Data frame:
1. 0th col 1st_col 2nd_col
2. 23 46 6
3. 33 56 3
4. 243 2 21
The output data frame should be like:
Index
1. 0th col 1st_col 2nd_col
2. 6 23 46
3. 3 33 56
4. 2 21 243
The rows have to be sorted in ascending or descending order, Independent of columns, Means values for columns can be interchanged within the same row, to sort the row. Sorting rows in the following unique manner.
Please Help, I am in the middle of something very important.
Convert DataFrame to numpy array and sort by np.sort with axis=1, then create DataFrame by constructor:
df1 = pd.DataFrame(np.sort(df.to_numpy(), axis=1),
index=df.index,
columns=df.columns)
print (df1)
0th col 1st_col 2nd_col
1 6 23 46
2 3 33 56
3 2 21 243
Related
I have a DataFrame like this, where for column2 I need to add 0.004 throughout the column to get a 0 value in row 1 of column 2. Similarly, for column 3 I need to subtract 0.4637 from the entire column to get a 0 value at row 1 column 3. How do I efficiently execute this?
Here is my code -
df2 = pd.DataFrame(np.zeros((df.shape[0], len(df.columns)))).round(0).astype(int)
for (i,j) in zip(range(0, 5999),range(1,len(df.columns))):
if j==1:
df2.values[i,j] = df.values[i,j] + df.values[0,1]
elif j>1:
df2.iloc[i,j] = df.iloc[i,j] - df.iloc[0,j]
print(df2)
Any help would be greatly appreciated. Thank you.
df2 = df - df.iloc[0]
Explanation:
Let's work through an example.
df = pd.DataFrame(np.arange(20).reshape(4, 5))
0
1
2
3
4
0
0
1
2
3
4
1
5
6
7
8
9
2
10
11
12
13
14
3
15
16
17
18
19
df.iloc[0] selects the first row of the dataframe:
0 0
1 1
2 2
3 3
4 4
Name: 0, dtype: int64
This is a Series. The first column printed here is its index (column names of the dataframe), and the second one - the actual values of the first row of the dataframe.
We can convert it to a list to better see its values
df.iloc[0].tolist()
[0, 1, 2, 3, 4]
Then, using broadcasting, we are subtracting each value from the whole column where it has come from.
I have a dataframe with currently 22 rows
index value
0 23
1 22
2 19
...
21 20
to this dataframe, i want to add 72 rows to make the dataframe exactly 100 rows. So i need to fill loc[22:99] but with a certain value, let's say 100.
I tried something like this
uncon_dstn_2021['balance'].loc[22:99] = 100
but did not work. Any idea?
You can do reindex
out = df.reindex(df.index.tolist() + list(range(22, 99+1)), fill_value = 100)
You can also use pd.concat:
df1 = pd.concat([df, pd.DataFrame({'balance': [100]*(100-len(df))})], ignore_index=True)
print(df1)
# Output
balance
0 1
1 14
2 11
3 11
4 10
.. ...
96 100
97 100
98 100
99 100
[100 rows x 1 columns]
in my data frame I want to iterrows() of two columns but want to save result in 1 column.for example df is
x y
5 10
30 445
70 32
expected output is
points sequence
5 1
10 2
30 1
445 2
I know about iterrows() but it saved out put in two different columns.How can I get expected output and is there any way to generate sequence number according to condition? any help will be appreciated.
First never use iterrows, because really slow.
If want 1, 2 sequence by number of columns convert values to numy array by DataFrame.to_numpy and add numpy.ravel, then for sequence use numpy.tile:
df = pd.DataFrame({'points': df.to_numpy().ravel(),
'sequence': np.tile([1,2], len(df))})
print (df)
points sequence
0 5 1
1 10 2
2 30 1
3 445 2
4 70 1
5 32 2
Do this way:
>>> pd.DataFrame([i[1] for i in df.iterrows()])
points sequence
0 5 1
1 10 2
2 30 1
3 445 2
I have two dataframe, please tell me how I can compare them by operator name, if it matches, then add the values of quantity and time to the first data frame.
In [2]: df1 In [3]: df2
Out[2]: Out[3]:
Name count time Name count time
0 Bob 123 4:12:10 0 Rick 9 0:13:00
1 Alice 99 1:01:12 1 Jone 7 0:24:21
2 Sergei 78 0:18:01 2 Bob 10 0:15:13
85 rows x 3 columns 105 rows x 3 columns
I want to get:
In [5]: df1
Out[5]:
Name count time
0 Bob 133 4:27:23
1 Alice 99 1:01:12
2 Sergei 78 0:18:01
85 rows x 3 columns
Use set_index and add them together. Finally, update back.
df1 = df1.set_index('Name')
df1.update(df1 + df2.set_index('Name'))
df1 = df1.reset_index()
Out[759]:
Name count time
0 Bob 133.0 04:27:23
1 Alice 99.0 01:01:12
2 Sergei 78.0 00:18:01
Note: I assume time columns in both df1 and df2 are already in correct date/time format. If they are in string format, you need to convert them before running above commands as follows:
df1.time = pd.to_timedelta(df1.time)
df2.time = pd.to_timedelta(df2.time)
I need to take log values of each element of a column in a DataFrame.Also i want to add the resulted column to the previous dataframe.
This is my dataframe
df1=pd.read_csv('doctors.csv',encoding='latin-1')
These are the columns
Index(['PatientID', 'Pregnancies', 'PlasmaGlucose', 'DiastolicBloodPressure',
'TricepsThickness', 'SerumInsulin', 'BMI', 'DiabetesPedigree', 'Age',
'Diabetic', 'Physician'],
dtype='object')
I want to form a new column of logarithmic values for 'Age' column.
I believe need numpy.log10:
df = pd.DataFrame({'Age':[10,22,33,44,34,56,34]})
df['log'] = np.log10(df['Age'])
print (df)
Age log
0 10 1.000000
1 22 1.342423
2 33 1.518514
3 44 1.643453
4 34 1.531479
5 56 1.748188
6 34 1.531479