I have a similar doubt to the one in the mentioned link. Instead of returning column names in a list, I want column names in the format dtype:object.
For example,
A
B
C
D
Name:x,dtype:object
I am using Excel file in xlsx format.
Link: Get list from pandas DataFrame column headers
I think you need read_excel first for df and then Series constructor or Index.to_series for Series from column names:
df = pd.DataFrame({'A':[1,2,3],
'B':[4,5,6],
'C':[7,8,9],
'D':[1,3,5]})
print (df)
A B C D
0 1 4 7 1
1 2 5 8 3
2 3 6 9 5
s = pd.Series(df.columns.values, name='x')
print (s)
0 A
1 B
2 C
3 D
Name: x, dtype: object
s1 = df.columns.to_series().rename('x')
print (s1)
A A
B B
C C
D D
Name: x, dtype: object
Related
I've been trying to figure this problem for a couple of hours now and seem to reach a dead end everytime. A small example of what I want to do is shown below.
Normal Series
a
b
c
d
Duplicated Series
a
a
b
b
c
c
d
d
Try with loc and df.index.repeat:
>>> df.loc[df.index.repeat(2)]
Normal Series
0 a
0 a
1 b
1 b
2 c
2 c
3 d
3 d
>>>
Or with reset_index:
>>> df.loc[df.index.repeat(2)].reset_index(drop=True)
Normal Series
0 a
1 a
2 b
3 b
4 c
5 c
6 d
7 d
>>>
You can just concat a duplicated series together and sort it.
sample = pd.Series(['a','b','c','d'])
output = pd.concat([sample,sample]).sort_values().reset_index(drop=True)
output
I have a dataframe which I want to plot with matplotlib, but the index column is the time and I cannot plot it.
This is the dataframe (df3):
but when I try the following:
plt.plot(df3['magnetic_mag mean'], df3['YYYY-MO-DD HH-MI-SS_SSS'], label='FDI')
I'm getting an error obviously:
KeyError: 'YYYY-MO-DD HH-MI-SS_SSS'
So what I want to do is to add a new extra column to my dataframe (named 'Time) which is just a copy of the index column.
How can I do it?
This is the entire code:
#Importing the csv file into df
df = pd.read_csv('university2.csv', sep=";", skiprows=1)
#Changing datetime
df['YYYY-MO-DD HH-MI-SS_SSS'] = pd.to_datetime(df['YYYY-MO-DD HH-MI-SS_SSS'],
format='%Y-%m-%d %H:%M:%S:%f')
#Set index from column
df = df.set_index('YYYY-MO-DD HH-MI-SS_SSS')
#Add Magnetic Magnitude Column
df['magnetic_mag'] = np.sqrt(df['MAGNETIC FIELD X (μT)']**2 + df['MAGNETIC FIELD Y (μT)']**2 + df['MAGNETIC FIELD Z (μT)']**2)
#Subtract Earth's Average Magnetic Field from 'magnetic_mag'
df['magnetic_mag'] = df['magnetic_mag'] - 30
#Copy interesting values
df2 = df[[ 'ATMOSPHERIC PRESSURE (hPa)',
'TEMPERATURE (C)', 'magnetic_mag']].copy()
#Hourly Average and Standard Deviation for interesting values
df3 = df2.resample('H').agg(['mean','std'])
df3.columns = [' '.join(col) for col in df3.columns]
df3.reset_index()
plt.plot(df3['magnetic_mag mean'], df3['YYYY-MO-DD HH-MI-SS_SSS'], label='FDI')
Thank you !!
I think you need reset_index:
df3 = df3.reset_index()
Possible solution, but I think inplace is not good practice, check this and this:
df3.reset_index(inplace=True)
But if you need new column, use:
df3['new'] = df3.index
I think you can read_csv better:
df = pd.read_csv('university2.csv',
sep=";",
skiprows=1,
index_col='YYYY-MO-DD HH-MI-SS_SSS',
parse_dates='YYYY-MO-DD HH-MI-SS_SSS') #if doesnt work, use pd.to_datetime
And then omit:
#Changing datetime
df['YYYY-MO-DD HH-MI-SS_SSS'] = pd.to_datetime(df['YYYY-MO-DD HH-MI-SS_SSS'],
format='%Y-%m-%d %H:%M:%S:%f')
#Set index from column
df = df.set_index('YYYY-MO-DD HH-MI-SS_SSS')
EDIT: If MultiIndex or Index is from groupby operation, possible solutions are:
df = pd.DataFrame({'A':list('aaaabbbb'),
'B':list('ccddeeff'),
'C':range(8),
'D':range(4,12)})
print (df)
A B C D
0 a c 0 4
1 a c 1 5
2 a d 2 6
3 a d 3 7
4 b e 4 8
5 b e 5 9
6 b f 6 10
7 b f 7 11
df1 = df.groupby(['A','B']).sum()
print (df1)
C D
A B
a c 1 9
d 5 13
b e 9 17
f 13 21
Add parameter as_index=False:
df2 = df.groupby(['A','B'], as_index=False).sum()
print (df2)
A B C D
0 a c 1 9
1 a d 5 13
2 b e 9 17
3 b f 13 21
Or add reset_index:
df2 = df.groupby(['A','B']).sum().reset_index()
print (df2)
A B C D
0 a c 1 9
1 a d 5 13
2 b e 9 17
3 b f 13 21
You can directly access in the index and get it plotted, following is an example:
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
df = pd.DataFrame(np.random.randn(1000), index=pd.date_range('1/1/2000', periods=1000))
#Get index in horizontal axis
plt.plot(df.index, df[0])
plt.show()
#Get index in vertiacal axis
plt.plot(df[0], df.index)
plt.show()
You can also use eval to achieve this:
In [2]: df = pd.DataFrame({'num': range(5), 'date': pd.date_range('2022-06-30', '2022-07-04')}, index=list('ABCDE'))
In [3]: df
Out[3]:
num date
A 0 2022-06-30
B 1 2022-07-01
C 2 2022-07-02
D 3 2022-07-03
E 4 2022-07-04
In [4]: df.eval('index_copy = index')
Out[4]:
num date index_copy
A 0 2022-06-30 A
B 1 2022-07-01 B
C 2 2022-07-02 C
D 3 2022-07-03 D
E 4 2022-07-04 E
Can anyone help with some code that will achieve the following transformation? I have tried variations of df.melt, df.explode, and also a looping statement but only get error statements. I think it might need nesting but don't have the experience to do so.
index A B C D
0 X d 4 2
1 Y b 5 2
Where column D represents frequency of column C.
desired output is:
index A B C
0 X d 4
1 X d 4
2 Y b 5
3 Y b 5
If you want to repeat rows, why not use index.repeat?
import pandas as pd
#recreate the sample dataframe
df = pd.DataFrame({"A":["X","Y"],"B":["d","b"],"C":[4,5],"D":[3,2]}, columns=list("ABCD"))
df = df.reindex(df.index.repeat(df["D"])).drop("D", 1).reset_index(drop=True)
print(df)
Sample output
A B C
0 X d 4
1 X d 4
2 X d 4
3 Y b 5
4 Y b 5
How can I drop the whole group by city and district if date's value of 2018/11/1 not exits in the following dataframe:
city district date value
0 a c 2018/9/1 12
1 a c 2018/10/1 4
2 a c 2018/11/1 5
3 b d 2018/9/1 3
4 b d 2018/10/1 7
The expected result will like this:
city district date value
0 a c 2018/9/1 12
1 a c 2018/10/1 4
2 a c 2018/11/1 5
Thank you!
Create helper column by DataFrame.assign, compare by datetime and test if at least one true per groups with GroupBy.any and GroupBy.transform for possible filter by boolean indexing:
mask = (df.assign(new=df['date'].eq('2018/11/1'))
.groupby(['city','district'])['new'].transform('any'))
df = df[mask]
print (df)
city district date value
0 a c 2018/9/1 12
1 a c 2018/10/1 4
2 a c 2018/11/1 5
If error with misisng values in mask one possivle idea is replace misisng values in columns used for groups:
mask = (df.assign(new=df['date'].eq('2018/11/1'),
city= df['city'].fillna(-1),
district= df['district'].fillna(-1))
.groupby(['city','district'])['new'].transform('any'))
df = df[mask]
print (df)
city district date value
0 a c 2018/9/1 12
1 a c 2018/10/1 4
2 a c 2018/11/1 5
Another idea is add possible misisng index values by reindex and also replace missing values to False:
mask = (df.assign(new=df['date'].eq('2018/11/1'))
.groupby(['city','district'])['new'].transform('any'))
df = df[mask.reindex(df.index, fill_value=False).fillna(False)]
print (df)
city district date value
0 a c 2018/9/1 12
1 a c 2018/10/1 4
2 a c 2018/11/1 5
There's a special GroupBy.filter() method for this. Assuming date is already datetime:
filter_date = pd.Timestamp('2018-11-01').date()
df = df.groupby(['city', 'district']).filter(lambda x: (x['date'].dt.date == filter_date).any())
I have a data-frame with 55 columns and 2 million rows having mix of categorical and numeric fileds. There are null/na values in the data-set. I want to fill Null values with Column names.
The data-set I have is:
A B C D .....
1 na na 3 .....
na 3 4 na .....
........................
The output the I am trying to get is:
A B C D .....
1 B C 3 .....
A 3 4 D .....
........................
I am trying to use :
df.fillna(method='ffill')
Is there another way?
Python:3.6.5
Use DataFrame.fillna with columns converted to Series by Index.to_series:
df = df.fillna(df.columns.to_series())
print (df)
A B C D
0 1 B C 3
1 A 3 4 D
EDIT: If categorical columns in DataFrame select these columns and append non exist values by cat.add_categories:
for c in df.select_dtypes('category'):
df[c] = df[c].cat.add_categories(c)
df = df.fillna(df.columns.to_series())