import pandas as pd
import calendar

method='cubic'
order=None

N = int(input())
headers = input().split()
data = [input().split() for _ in range(N)]

df = pd.DataFrame(data,columns = headers)

df["date"] = df["yyyy"].str.strip()+" "+df["month"].str.strip()
df["date"] = pd.to_datetime(df["date"])
df = df.drop(["yyyy","month"],axis=1)
df["tmax_copy"] = df["tmax"]
df["tmin_copy"] = df["tmin"]
df["tmax_was_na"] = df["tmax"].apply(lambda x: True if "Missing" in x else False)
df["tmin_was_na"] = df["tmin"].apply(lambda x: True if "Missing" in x else False)

df["tmax"] = df["tmax"].apply(lambda x: None if "Missing" in x else x)
df["tmin"] = df["tmin"].apply(lambda x: None if "Missing" in x else x)

df.index = df["date"]
df = df.drop("date",axis=1)
df["tmax"] = df["tmax"].astype(float)
df["tmin"] = df["tmin"].astype(float)
df["tmax"] = df["tmax"].interpolate(method=method,order=order)
df["tmin"] = df["tmin"].interpolate(method=method,order=order)
df_fin = df[df["tmax_was_na"]|df["tmin_was_na"]][["tmin","tmax","tmin_copy","tmax_copy"]]
df_final = pd.DataFrame()
df_final2 = pd.DataFrame()
df_final["miss_val"] = df_fin[df_fin["tmax_copy"].str.contains("Missing")].tmax
df_final["miss_name"] = df_fin[df_fin["tmax_copy"].str.contains("Missing")].tmax_copy
df_final2["miss_val"] = df_fin[df_fin["tmin_copy"].str.contains("Missing")].tmin
df_final2["miss_name"] = df_fin[df_fin["tmin_copy"].str.contains("Missing")].tmin_copy
df_res = df_final.append(df_final2)
df_res["miss_id"] = df_res["miss_name"].str.split("_").str[1].astype(int)
print(*df_res.sort_values(by="miss_id").miss_val.to_list(),sep="\n")

import numpy as np
import pandas as pd

N = int(input())
input()

data = []
missing = []
for i in range(N):
    line = input()
    year, month, t_min, t_max = line.split()
    if 'Missing' in t_min:
        t_min = np.NaN
        missing.append((i,'t_min'))
    if 'Missing' in t_max:
        t_max = np.NaN
        missing.append((i,'t_max'))
    data.append([float(t_min), float(t_max)])
    
df = pd.DataFrame(data, columns=['t_min','t_max'])
df = df.interpolate('cubic', limit_direction='both')

for i,col in missing:
    print(round(df.iloc[i][col],2))

import pandas as pd

N = int(input())

columns = input().split()

df = pd.DataFrame(columns=columns)

for i in range(N):
    df.loc[i, :] = input().split()
    
df.tmax = pd.to_numeric(df.tmax, errors='coerce')
df.tmin = pd.to_numeric(df.tmin, errors='coerce')

tmax_missing = df.tmax.isna()
tmin_missing = df.tmin.isna()

tmax_interp = df.tmax.interpolate(method='cubic')
tmin_interp = df.tmin.interpolate(method='cubic')

for i in range(df.shape[0]):
    if tmax_missing[i]:
        print(tmax_interp[i])
    if tmin_missing[i]:
        print(tmin_interp[i])

Custom checker Failed: Success

Traceback (most recent call last):
  File "/custom-D5UXWlZg7Y7Vtpf6S1QY/solution.py", line 183, in <module>
    run_custom_checker(t_obj, r_obj)
  File "/custom-D5UXWlZg7Y7Vtpf6S1QY/solution.py", line 94, in run_custom_checker
    return WA(r_obj,score)
  File "/custom-D5UXWlZg7Y7Vtpf6S1QY/solution.py", line 44, in WA
    obj.message = "Wrong Answer\n" + msg
TypeError: coercing to Unicode: need string or buffer, float found