assertThat(TWO_VALUES_PAIRED_STATS.populationCovariance())
        .isWithin(ALLOWED_ERROR)
        .of(TWO_VALUES_SUM_OF_PRODUCTS_OF_DELTAS / 2);
    // For datasets of many double values, we test many combinations of finite and non-finite
    // x-values:
    for (ManyValues values : ALL_MANY_VALUES) {
      PairedStats stats = createPairedStatsOf(values.asIterable(), OTHER_MANY_VALUES);

    assertThat(TWO_VALUES_PAIRED_STATS.populationCovariance())
        .isWithin(ALLOWED_ERROR)
        .of(TWO_VALUES_SUM_OF_PRODUCTS_OF_DELTAS / 2);
    // For datasets of many double values, we test many combinations of finite and non-finite
    // x-values:
    for (ManyValues values : ALL_MANY_VALUES) {
      PairedStats stats = createPairedStatsOf(values.asIterable(), OTHER_MANY_VALUES);

## Intro { #intro }

**FastAPI** wouldn't exist if not for the previous work of others.

There have been many tools created before that have helped inspire its creation.

I have been avoiding the creation of a new framework for several years. First I tried to solve all the features covered by **FastAPI** using many different frameworks, plug-ins, and tools.

    assertThat(manyValuesAccumulatorByAddAllPartitionedPairedStats.populationCovariance())
        .isWithin(ALLOWED_ERROR)
        .of(MANY_VALUES_SUM_OF_PRODUCTS_OF_DELTAS / MANY_VALUES_COUNT);
    // For datasets of many double values, we test many combinations of finite and non-finite
    // x-values:
    for (ManyValues values : ALL_MANY_VALUES) {
      PairedStatsAccumulator accumulator =

So, the thief won't be able to try to use that password in another system (as many users use the same password everywhere, this would be dangerous).

## Install `pwdlib` { #install-pwdlib }

pwdlib is a great Python package to handle password hashes.

It supports many secure hashing algorithms and utilities to work with them.

The recommended algorithm is "Argon2".

    assertThat(manyValuesAccumulatorByAddAllPartitionedPairedStats.populationCovariance())
        .isWithin(ALLOWED_ERROR)
        .of(MANY_VALUES_SUM_OF_PRODUCTS_OF_DELTAS / MANY_VALUES_COUNT);
    // For datasets of many double values, we test many combinations of finite and non-finite
    // x-values:
    for (ManyValues values : ALL_MANY_VALUES) {
      PairedStatsAccumulator accumulator =

        .of(MANY_VALUES_MEAN);
    assertThat(manyValuesAccumulatorByAddAllStatsAccumulator.mean())
        .isWithin(ALLOWED_ERROR)
        .of(MANY_VALUES_MEAN);
    // For datasets of many double values created from an iterable, we test many combinations of
    // finite and non-finite values:
    for (ManyValues values : ALL_MANY_VALUES) {
      StatsAccumulator accumulator = new StatsAccumulator();

	user.Pets = append(user.Pets, &pet)
	CheckUser(t, user2, user)

	AssertAssociationCount(t, user, "Pets", 3, "AfterAppend")

	pets2 := []Pet{{Name: "pet-has-many-append-1-1"}, {Name: "pet-has-many-append-1-1"}}

	if err := DB.Model(&user2).Association("Pets").Append(&pets2); err != nil {
		t.Fatalf("Error happened when append pet, got %v", err)
	}

	for _, pet := range pets2 {
		pet := pet
		if pet.ID == 0 {

        return value;
      }
    }
    return null;
  }

  /**
   * A sensible definition of {@link #clear} in terms of the {@code iterator} method of {@link
   * #entrySet}. In many cases, you may wish to override {@link #clear} to forward to this
   * implementation.
   *
   * @since 7.0
   */
  protected void standardClear() {
    Iterators.clear(entrySet().iterator());
  }

        return value;
      }
    }
    return null;
  }

  /**
   * A sensible definition of {@link #clear} in terms of the {@code iterator} method of {@link
   * #entrySet}. In many cases, you may wish to override {@link #clear} to forward to this
   * implementation.
   *
   * @since 7.0
   */
  protected void standardClear() {
    Iterators.clear(entrySet().iterator());
  }