@@ -1321,8 +1321,8 @@ def idxmin(self, skipna: bool = True) -> Series:
13211321
13221322 Returns
13231323 -------
1324- Index
1325- Label of the minimum value .
1324+ Series
1325+ Indexes of minima in each group .
13261326
13271327 Raises
13281328 ------
@@ -1374,8 +1374,8 @@ def idxmax(self, skipna: bool = True) -> Series:
13741374
13751375 Returns
13761376 -------
1377- Index
1378- Label of the maximum value .
1377+ Series
1378+ Indexes of maxima in each group .
13791379
13801380 Raises
13811381 ------
@@ -2512,8 +2512,8 @@ def idxmax(
25122512
25132513 Returns
25142514 -------
2515- Series
2516- Indexes of maxima in each group.
2515+ DataFrame
2516+ Indexes of maxima in each column according to the group.
25172517
25182518 Raises
25192519 ------
@@ -2523,6 +2523,7 @@ def idxmax(
25232523 See Also
25242524 --------
25252525 Series.idxmax : Return index of the maximum element.
2526+ DataFrame.idxmax : Indexes of maxima along the specified axis.
25262527
25272528 Notes
25282529 -----
@@ -2536,6 +2537,7 @@ def idxmax(
25362537 ... {
25372538 ... "consumption": [10.51, 103.11, 55.48],
25382539 ... "co2_emissions": [37.2, 19.66, 1712],
2540+ ... "food_type": ["meat", "plant", "meat"],
25392541 ... },
25402542 ... index=["Pork", "Wheat Products", "Beef"],
25412543 ... )
@@ -2546,12 +2548,14 @@ def idxmax(
25462548 Wheat Products 103.11 19.66
25472549 Beef 55.48 1712.00
25482550
2549- By default, it returns the index for the maximum value in each column.
2551+ By default, it returns the index for the maximum value in each column
2552+ according to the group.
25502553
2551- >>> df.idxmax()
2552- consumption Wheat Products
2553- co2_emissions Beef
2554- dtype: object
2554+ >>> df.groupby("food_type").idxmax()
2555+ consumption co2_emissions
2556+ food_type
2557+ animal Beef Beef
2558+ plant Wheat Products Wheat Products
25552559 """
25562560 return self ._idxmax_idxmin ("idxmax" , numeric_only = numeric_only , skipna = skipna )
25572561
@@ -2574,8 +2578,8 @@ def idxmin(
25742578
25752579 Returns
25762580 -------
2577- Series
2578- Indexes of minima in each group.
2581+ DataFrame
2582+ Indexes of minima in each column according to the group.
25792583
25802584 Raises
25812585 ------
@@ -2585,6 +2589,7 @@ def idxmin(
25852589 See Also
25862590 --------
25872591 Series.idxmin : Return index of the minimum element.
2592+ DataFrame.idxmin : Indexes of minima along the specified axis.
25882593
25892594 Notes
25902595 -----
@@ -2598,6 +2603,7 @@ def idxmin(
25982603 ... {
25992604 ... "consumption": [10.51, 103.11, 55.48],
26002605 ... "co2_emissions": [37.2, 19.66, 1712],
2606+ ... "food_type": ["meat", "plant", "meat"],
26012607 ... },
26022608 ... index=["Pork", "Wheat Products", "Beef"],
26032609 ... )
@@ -2608,12 +2614,14 @@ def idxmin(
26082614 Wheat Products 103.11 19.66
26092615 Beef 55.48 1712.00
26102616
2611- By default, it returns the index for the minimum value in each column.
2617+ By default, it returns the index for the minimum value in each column
2618+ according to the group.
26122619
2613- >>> df.idxmin()
2614- consumption Pork
2615- co2_emissions Wheat Products
2616- dtype: object
2620+ >>> df.groupby("food_type").idxmin()
2621+ consumption co2_emissions
2622+ food_type
2623+ animal Pork Pork
2624+ plant Wheat Products Wheat Products
26172625 """
26182626 return self ._idxmax_idxmin ("idxmin" , numeric_only = numeric_only , skipna = skipna )
26192627
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