I have two pandas dataframes:
import pandas as pd
df_1 = pd.DataFrame({'ID': [1, 2, 4, 7, 30],
'Instrument': ['temp', 'temp_sensor', 'temp_sensor',
'sensor', 'sensor'],
'Value': [1000, 0, 1000, 0, 1000]})
print(df_1)
ID Instrument Value
1 temp 1000
2 temp_sensor 0
4 temp_sensor 1000
7 sensor 0
30 sensor 1000
df_2 = pd.DataFrame({'ID': [1, 30],
'Instrument': ['temp', 'sensor'],
'Value': [1000, 1000]})
print(df_2)
ID Instrument Value
1 temp 1000
30 sensor 1000
I need to exclude from df_1 the lines that also exist in df_2. So I made the code:
combined = df_1.append(df_2)
combined[~combined.index.duplicated(keep=False)]
The (wrong) output is:
ID Instrument Value
4 temp_sensor 1000
7 sensor 0
30 sensor 1000
I would like the output to be:
ID Instrument Value
2 temp_sensor 0
4 temp_sensor 1000
7 sensor 0
I relied on what was explained in: How to remove a pandas dataframe from another dataframe
Use DataFrame.merge by all columns names with left join and parameter indicator=True and filter rows with left_only values:
s = df_1.merge(df_2, on=list(df_1.columns), how='left', indicator=True)['_merge']
df = df_1.loc[s == 'left_only']
print(df)
ID Instrument Value
1 2 temp_sensor 0
2 4 temp_sensor 1000
3 7 sensor 0
Related
After groupby by "Mode" column and take out the value from "indicator" of "max, min", how to let the relative value to show in the same dataframe like below:
df = pd.read_csv(r'relative.csv')
Grouped = df.groupby('Mode')['Indicator'].agg(['max', 'min'])
print(Grouped)
(from google, maybe can use from col_value or row_value function, but seem be more complicated, could someone can help to solve it by easy ways? thank you.)
You can do it in two steps, using groupby and idxmin() or idxmix():
# Create a df with the min values of 'Indicator', renaming the column 'Value' to 'B'
min = df.loc[df.groupby('Mode')['Indicator'].idxmin()].reset_index(drop=True).rename(columns={'Indicator': 'min', 'Value': 'B'})
print(min)
# Mode min B
# 0 A 1 6
# 1 B 1 7
# Create a df with the max values of 'Indicator', renaming the column 'Value' to 'A'
max = df.loc[df.groupby('Mode')['Indicator'].idxmax()].reset_index(drop=True).rename(columns={'Indicator': 'max', 'Value': 'A'})
print(max)
# Mode max A
# 0 A 3 2
# 1 B 4 3
# Merge the dataframes together
result = pd.merge(min, max)
# reorder the columns to match expected output
print(result[['Mode', 'max','min','A', 'B']])
# Mode max min A B
# 0 A 3 1 2 6
# 1 B 4 1 3 7
The logic is unclear, there is no real reason why you would call your columns A/B since the 6/3 values in it are not coming from A/B.
I assume you want to achieve:
(df.groupby('Mode')['Indicator'].agg(['idxmax', 'idxmin'])
.rename(columns={'idxmin': 'min', 'idxmax': 'max'}).stack()
.to_frame('x').merge(df, left_on='x', right_index=True)
.drop(columns=['x', 'Mode']).unstack()
)
Output:
Indicator Value
max min max min
Mode
A 3 1 2 6
B 4 1 3 7
C 10 10 20 20
Used input:
Mode Indicator Value
0 A 1 6
1 A 2 5
2 A 3 2
3 B 4 3
4 B 3 6
5 B 2 8
6 B 1 7
7 C 10 20
With the dataframe you provided:
import pandas as pd
df = pd.DataFrame(
{
"Mode": ["A", "A", "A", "B", "B", "B", "B"],
"Indicator": [1, 2, 3, 4, 3, 2, 1],
"Value": [6, 5, 2, 3, 6, 8, 7],
}
)
new_df = df.groupby("Mode")["Indicator"].agg(["max", "min"])
print(new_df)
# Output
max min
Mode
A 3 1
B 4 1
Here is one way to do it with product from Python standard library's itertools module and Pandas at property:
from itertools import product
for row, (col, func) in product(["A", "B"], [("A", "max"), ("B", "min")]):
new_df.at[row, col] = df.loc[
(df["Mode"] == row) & (df["Indicator"] == new_df.loc[row, func]), "Value"
].values[0]
new_df = new_df.astype(int)
Then:
print(new_df)
# Output
max min A B
Mode
A 3 1 2 6
B 4 1 3 7
I have the following dataframe in pandas:
import pandas as pd
df = pd.DataFrame({'Sensor': ['strain', 'water', 'water', 'ultrassonic', 'strain'],
'Total': [1,10,20,5,9],
'Columns_3': ['A', 'B', 'C', 'D', 'E']})
print(df)
Sensor Total Columns_3
strain 1 A
water 10 B
water 20 C
ultrassonic 5 D
strain 9 E
I'd like to do a count of the total amount of different sensors. So I tried to use groupby() as follows:
df_result = df.groupby(['Sensor', 'Total'])['Total'].sum()
The output of my code is:
Sensor Total
strain 1 1
9 9
ultrassonic 5 5
water 10 10
20 20
Name: Total, dtype: int64
However, I would like the output to be a dataframe (df_result) as follows:
print(df_result)
Sensor Total_final
strain 10
water 30
ultrassonic 5
you don't need total as part of the group by
df.groupby(['Sensor'])['Total'].sum()
Sensor
strain 10
ultrassonic 5
water 30
Name: Total, dtype: int64
You can just groupby with Sensor only
df_result = df.groupby(['Sensor'])['Total'].sum().reset_index()
df = pd.DataFrame({'a': [1,2,3], 'b': [4,5,6]})
produces
a b
0 1 4
1 2 5
2 3 6
Given a dict
d = {'a': 2, 'b': 5}
how would I extract the rows of the dataframe where the dict's keys values match all the column values -- so in this case
a b
1 2 5
You can compare with Series and filter:
df[(df == pd.Series(d)).all(1)]
a b
1 2 5
This comparison is aligned on the index/columns and broadcasted for each row.
Compare the values and use indexing,
df[ (df.values == np.array(list(d.values()))).all(1) ]
a b
1 2 5
I am trying to calculate multiple colums from multiple columns in a pandas dataframe using a function.
The function takes three arguments -a-, -b-, and -c- and and returns three calculated values -sum-, -prod- and -quot-. In my pandas data frame I have three coumns -a-, -b- and and -c- from which I want to calculate the columns -sum-, -prod- and -quot-.
The mapping that I do works only when I have exactly three rows. I do not know what is going wrong, although I expect that it has to do something with selecting the correct axis. Could someone explain what is happening and how I can calculate the values that I would like to have.
Below are the situations that I have tested.
INITIAL VALUES
def sum_prod_quot(a,b,c):
sum = a + b + c
prod = a * b * c
quot = a / b / c
return (sum, prod, quot)
df = pd.DataFrame({ 'a': [20, 100, 18],
'b': [ 5, 10, 3],
'c': [ 2, 10, 6],
'd': [ 1, 2, 3]
})
df
a b c d
0 20 5 2 1
1 100 10 10 2
2 18 3 6 3
CALCULATION STEPS
Using exactly three rows
When I calculate three columns from this dataframe and using the function function I get:
df['sum'], df['prod'], df['quot'] = \
list( map(sum_prod_quot, df['a'], df['b'], df['c']))
df
a b c d sum prod quot
0 20 5 2 1 27.0 120.0 27.0
1 100 10 10 2 200.0 10000.0 324.0
2 18 3 6 3 2.0 1.0 1.0
This is exactly the result that I want to have: The sum-column has the sum of the elements in the columns a,b,c; the prod-column has the product of the elements in the columns a,b,c and the quot-column has the quotients of the elements in the columns a,b,c.
Using more than three rows
When I expand the dataframe with one row, I get an error!
The data frame is defined as:
df = pd.DataFrame({ 'a': [20, 100, 18, 40],
'b': [ 5, 10, 3, 10],
'c': [ 2, 10, 6, 4],
'd': [ 1, 2, 3, 4]
})
df
a b c d
0 20 5 2 1
1 100 10 10 2
2 18 3 6 3
3 40 10 4 4
The call is
df['sum'], df['prod'], df['quot'] = \
list( map(sum_prod_quot, df['a'], df['b'], df['c']))
The result is
...
list( map(sum_prod_quot, df['a'], df['b'], df['c']))
ValueError: too many values to unpack (expected 3)
while I would expect an extra row:
df
a b c d sum prod quot
0 20 5 2 1 27.0 120.0 27.0
1 100 10 10 2 200.0 10000.0 324.0
2 18 3 6 3 2.0 1.0 1.0
3 40 10 4 4 54.0 1600.0 1.0
Using less than three rows
When I reduce tthe dataframe with one row I get also an error.
The dataframe is defined as:
df = pd.DataFrame({ 'a': [20, 100],
'b': [ 5, 10],
'c': [ 2, 10],
'd': [ 1, 2]
})
df
a b c d
0 20 5 2 1
1 100 10 10 2
The call is
df['sum'], df['prod'], df['quot'] = \
list( map(sum_prod_quot, df['a'], df['b'], df['c']))
The result is
...
list( map(sum_prod_quot, df['a'], df['b'], df['c']))
ValueError: need more than 2 values to unpack
while I would expect a row less:
df
a b c d sum prod quot
0 20 5 2 1 27.0 120.0 27.0
1 100 10 10 2 200.0 10000.0 324.0
QUESTIONS
The questions I have:
1) Why do I get these errors?
2) How do I have to modify the call such that I get the desired data frame?
NOTE
In this link a similar question is asked, but the given answer did not work for me.
The answer doesn't seem correct for 3 rows as well. Can you check other values except first row and first column. Looking at the results, product of 20*5*2 is NOT 120, it's 200 and is placed below in sum column. You need to form list in correct way before assigning to new columns. You can try use following to set the new columns:
df['sum'], df['prod'], df['quot'] = zip(*map(sum_prod_quot, df['a'], df['b'], df['c']))
For details follow the link
I am having a tough time with this one - not sure why...maybe it's the late hour.
I have a dataframe in pandas as follows:
1 10
2 11
3 20
4 5
5 10
I would like to calculate for each row the multiplicand for each row above it. For example, at row 3, I would like to calculate 10*11*20, or 2,200.
How do I do this?
Use cumprod.
Example:
df = pd.DataFrame({'A': [10, 11, 20, 5, 10]}, index=range(1, 6))
df['cprod'] = df['A'].cumprod()
Note, since your example is just a single column, a cumulative product can be done succinctly with a Series:
import pandas as pd
s = pd.Series([10, 11, 20, 5, 10])
s
# Output
0 10
1 11
2 20
3 5
4 10
dtype: int64
s.cumprod()
# Output
0 10
1 110
2 2200
3 11000
4 110000
dtype: int64
Kudos to #bananafish for locating the inherent cumprod method.