Getting the Minimum of the Rows in a Data Frame

How to get row-wise absolute minimum value in pandas dataframe

Use np.argmin (numpy counterpart of DataFrame.idxmin). Since you want to extract the original values, it's more convenient to access those values at the numpy level.

I added an extra row to your MRE for demonstration:

cols = np.argmin(df.abs().to_numpy(), axis=1) # [0, 2]
rows = range(len(cols))                       # [0, 1]
df['Result'] = df.to_numpy()[rows, cols]

#    Col1  Col2  Col3  Result
# 0    -1     2    -3      -1
# 1    10    -5     4       4

How to find the max, min value of ALL Dataframe [ not values by column neither rows ]

You can try:

max_val = df.max().max()

Get Row and Column with Minimum value in Entire Pandas DataFrame

Use:

a, b = df.stack().idxmin()
print(df.loc[[a], [b]])
    C2
R3   0

Another @John Zwinck solution working with missing values - use numpy.nanargmin:

df = pd.DataFrame(data=[[4,5,6],[2,np.nan,3],[7,0,5],[2,5,3]], 
    index = ['R1','R2','R3','R4'], 
    columns=['C1','C2','C3'])

print(df)
    C1   C2  C3
R1   4  5.0   6
R2   2  NaN   3
R3   7  0.0   5
R4   2  5.0   3

#https://stackoverflow.com/a/3230123
ri, ci = np.unravel_index(np.nanargmin(df.values), df.shape)
print(df.iloc[[ri], [ci]])
     C2
R3  0.0

Computing the minimum value for each row in a Pandas DataFrame

Use pandas.DataFrame.min with axis=1:

df['Dmin'] = df.min(axis=1)

min for each row in a data frame

You can use apply to go through each row

apply(df, 1, FUN = min)

Where 1 means to apply FUN to each row of df, 2 would mean to apply FUN to columns.

To remove missing values, use:

apply(df, 1, FUN = min, na.rm = TRUE)

pandas get the row-wise minimum value of two or more columns

If you are trying to get the row-wise mininum of two or more columns, use pandas.DataFrame.min. Note that by default axis=0; specifying axis=1 is necessary.

data['min_c_h'] = data[['flow_h','flow_c']].min(axis=1)

# display(data)
   flow_c  flow_d  flow_h  min_c_h
0      82      36      43       43
1      52      48      12       12
2      33      28      77       33
3      91      99      11       11
4      44      95      27       27
5       5      94      64        5
6      98       3      88       88
7      73      39      92       73
8      26      39      62       26
9      56      74      50       50

How to get the indexes of all minimum values in pandas dataframe?

You can use groupby+transform('min'):

s = df.stack()
s[s.eq(s.groupby(level=0).transform('min'))]

Output:

x  A    0
y  A    0
   B    0
z  B    0

Alternative format:

s = df.stack()
(s[s.eq(s.groupby(level=0).transform('min'))]
  .reset_index()
  .groupby('level_0')['level_1'].apply(list)
 )

Output:

level_0
x       [A]
y    [A, B]
z       [B]
Name: level_1, dtype: object

Substract minimum value of row from each element of row in dataframe,

You can also use the apply fonction with :

# 1 for the rows
apply(d, 1, function(x){x-min(x)}
# 2 for the columns 
apply(d, 2, function(x){x-min(x)}

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