How to zero values in column A of dataframe 1 if they are different from column A of dataframe 2 in pandas?

Question

I have two dataframes:

df1 = pd.DataFrame.from_dict({'Date': {1: '2021-01-02 00:00:00',
  2: '2021-01-03 00:00:00',
  3: '2021-01-04 00:00:00',
  4: '2021-01-05 00:00:00',
  5: '2021-01-06 00:00:00',
  6: '2021-01-07 00:00:00',
  7: '2021-01-08 00:00:00',
  8: '2021-01-09 00:00:00',
  9: '2021-01-10 00:00:00',
  10: '2021-01-11 00:00:00',
  11: '2021-01-12 00:00:00',
  12: '2021-01-13 00:00:00',
  13: '2021-01-14 00:00:00',
  14: '2021-01-15 00:00:00',
  15: '2021-01-16 00:00:00',
  16: '2021-01-17 00:00:00',
  17: '2021-01-18 00:00:00',
  18: '2021-01-19 00:00:00',
  19: '2021-01-20 00:00:00',
  20: '2021-01-21 00:00:00',
  21: '2021-01-22 00:00:00',
  22: '2021-01-23 00:00:00',
  23: '2021-01-24 00:00:00',
  24: '2021-01-25 00:00:00',
  25: '2021-01-26 00:00:00',
  26: '2021-01-27 00:00:00',
  27: '2021-01-28 00:00:00',
  28: '2021-01-29 00:00:00',
  29: '2021-01-30 00:00:00',
  30: '2021-01-31 00:00:00',
  31: '2021-02-01 00:00:00',
  32: '2021-02-02 00:00:00',
  33: '2021-02-03 00:00:00',
  34: '2021-02-04 00:00:00',
  35: '2021-02-05 00:00:00',
  36: '2021-02-06 00:00:00',
  37: '2021-02-07 00:00:00',
  38: '2021-02-08 00:00:00',
  39: '2021-02-09 00:00:00',
  40: '2021-02-10 00:00:00',
  41: '2021-02-11 00:00:00',
  42: '2021-02-12 00:00:00',
  43: '2021-02-13 00:00:00',
  44: '2021-02-14 00:00:00',
  45: '2021-02-15 00:00:00',
  46: '2021-02-16 00:00:00',
  47: '2021-02-17 00:00:00',
  48: '2021-02-18 00:00:00',
  49: '2021-02-19 00:00:00',
  50: '2021-02-20 00:00:00',
  51: '2021-02-21 00:00:00',
  52: '2021-02-22 00:00:00',
  53: '2021-02-23 00:00:00',
  54: '2021-02-24 00:00:00',
  55: '2021-02-25 00:00:00',
  56: '2021-02-26 00:00:00',
  57: '2021-02-27 00:00:00',
  58: '2021-02-28 00:00:00',
  59: '2021-03-01 00:00:00',
  60: '2021-03-02 00:00:00',
  61: '2021-03-03 00:00:00',
  62: '2021-03-04 00:00:00',
  63: '2021-03-05 00:00:00',
  64: '2021-03-06 00:00:00',
  65: '2021-03-07 00:00:00',
  66: '2021-03-08 00:00:00',
  67: '2021-03-09 00:00:00',
  68: '2021-03-10 00:00:00',
  69: '2021-03-11 00:00:00',
  70: '2021-03-12 00:00:00',
  71: '2021-03-13 00:00:00',
  72: '2021-03-14 00:00:00',
  73: '2021-03-15 00:00:00',
  74: '2021-03-16 00:00:00',
  75: '2021-03-17 00:00:00',
  76: '2021-03-18 00:00:00',
  77: '2021-03-19 00:00:00',
  78: '2021-03-20 00:00:00',
  79: '2021-03-21 00:00:00',
  80: '2021-03-22 00:00:00',
  81: '2021-03-23 00:00:00',
  82: '2021-03-24 00:00:00',
  83: '2021-03-25 00:00:00',
  84: '2021-03-26 00:00:00',
  85: '2021-03-27 00:00:00',
  86: '2021-03-28 00:00:00',
  87: '2021-03-29 00:00:00',
  88: '2021-03-30 00:00:00',
  89: '2021-03-31 00:00:00',
  90: '2021-04-01 00:00:00',
  91: '2021-04-02 00:00:00',
  92: '2021-04-03 00:00:00',
  93: '2021-04-04 00:00:00',
  94: '2021-04-05 00:00:00',
  95: '2021-04-06 00:00:00',
  96: '2021-04-07 00:00:00',
  97: '2021-04-08 00:00:00',
  98: '2021-04-09 00:00:00',
  99: '2021-04-10 00:00:00',
  100: '2021-04-11 00:00:00',
  101: '2021-04-12 00:00:00',
  102: '2021-04-13 00:00:00',
  103: '2021-04-14 00:00:00'},
 'Col': {1: 818,
  2: 712,
  3: 684,
  4: 642,
  5: 630,
  6: 273,
  7: 327,
  8: 365,
  9: 486,
  10: 890,
  11: 1003,
  12: 13060,
  13: 7456,
  14: 2897,
  15: 1550,
  16: 1018,
  17: 1006,
  18: 1302,
  19: 1239,
  20: 5023,
  21: 3132,
  22: 1448,
  23: 1120,
  24: 2862,
  25: 2042,
  26: 1390,
  27: 1281,
  28: 746,
  29: 482,
  30: 514,
  31: 642,
  32: 596,
  33: 1002,
  34: 869,
  35: 1053,
  36: 1377,
  37: 12167,
  38: 10190,
  39: 4252,
  40: 2648,
  41: 2536,
  42: 1617,
  43: 1776,
  44: 1235,
  45: 3080,
  46: 1299,
  47: 2344,
  48: 1739,
  49: 1028,
  50: 525,
  51: 1181,
  52: 1609,
  53: 861,
  54: 844,
  55: 622,
  56: 469,
  57: 801,
  58: 854,
  59: 1875,
  60: 1520,
  61: 823,
  62: 985,
  63: 1072,
  64: 538,
  65: 599,
  66: 2332,
  67: 1098,
  68: 743,
  69: 6889,
  70: 2107,
  71: 916,
  72: 617,
  73: 634,
  74: 589,
  75: 566,
  76: 2231,
  77: 1087,
  78: 772,
  79: 857,
  80: 1080,
  81: 660,
  82: 649,
  83: 956,
  84: 704,
  85: 680,
  86: 532,
  87: 539,
  88: 608,
  89: 2750,
  90: 1618,
  91: 589,
  92: 339,
  93: 451,
  94: 470,
  95: 419,
  96: 555,
  97: 1152,
  98: 890,
  99: 512,
  100: 411,
  101: 407,
  102: 773,
  103: 2116}})

df2 = pd.DataFrame.from_dict({'Date': {0: '2021-02-07 00:00:00',
  1: '2021-02-08 00:00:00',
  2: '2021-02-22 00:00:00',
  3: '2021-03-01 00:00:00',
  4: '2021-03-18 00:00:00',
  5: '2021-04-08 00:00:00',
  6: '2021-04-12 00:00:00'},
 'Titles': {0: '\xa0Interview with Le Journal du Dimanche ',
  1: '\xa0European\xa0Parliament plenary\xa0debate on the ECB Annual Report',
  2: '\xa0Investing in our climate, social and economic resilience',
  3: '\xa0The coronavirus crisis and SMEs',
  4: '\xa0Hearing of the Committee on Economic and Monetary Affairs of the European Parliament ',
  5: '\xa0IMFC Statement ',
  6: '\xa0Interview with CNBC'},
 'Author': {0: 'Christine Lagarde',
  1: 'Christine Lagarde',
  2: 'Christine Lagarde',
  3: 'Christine Lagarde',
  4: 'Christine Lagarde',
  5: 'Christine Lagarde',
  6: 'Christine Lagarde'},
 'Time': {0: '19:00',
  1: '17:15',
  2: '15:30',
  3: '17:10',
  4: '09:00',
  5: nan,
  6: nan}})

What I want to do is: if Date in df2 is equal to Date in df1, then leave the value of Col in df1 as it stands; if Date in df2 is different from Date in df1, then set the values of Col in df1 equal to 0. For example:

# this is the desired output: df1

Date               Col
2021-01-01          0
2021-01-02          0
2021-01-03          0
.                   .  
.                   .
.                   .
.                   .
2021-02-07        12167
....               ....

Can anyone help me with that?

Thanks!

Answer 1

Do you want this -

df1.loc[~df1['Date'].isin(df2.Date.to_numpy()), 'Col'] = 0

Will work without to_numpy() -

df1.loc[~df1['Date'].isin(df2.Date), 'Col'] = 0