下面的代码将仅生成正态分布的一个值,并使用相同的值填充所有缺失的值:
<?php
/**
* @file
* Contains onesignal_api.module.
*
*/
use Drupal\Core\Entity\EntityInterface;
/***
* Hook into OneSignal API to send push notifications once a new node is created
*/
function onesignal_api_insert(\Drupal\Core\Entity\EntityInterface $node) {
if($node->isNew()) {
function sendMessage() {
$content = array(
"en" => 'New Node Created'
);
$hashes_array = array();
array_push($hashes_array, array(
"id" => "like-button",
"text" => "Like",
"icon" => "http://i.imgur.com/N8SN8ZS.png",
"url" => "http://push-test/"
));
array_push($hashes_array, array(
"id" => "like-button-2",
"text" => "Like2",
"icon" => "http://i.imgur.com/N8SN8ZS.png",
"url" => "http://push-test/"
));
$fields = array(
'app_id' => "XXXXXXXXX",
'include_player_ids' => array("XXXXXX","XXXXX","XXXXXX"),
'data' => array(
"foo" => "bar"
),
'contents' => $content,
'web_buttons' => $hashes_array
);
$fields = json_encode($fields);
print("\nJSON sent:\n");
print($fields);
$ch = curl_init();
curl_setopt($ch, CURLOPT_URL, "https://onesignal.com/api/v1/notifications");
curl_setopt($ch, CURLOPT_HTTPHEADER, array(
'Content-Type: application/json; charset=utf-8',
'Authorization: Basic XXXXXXX'
));
curl_setopt($ch, CURLOPT_RETURNTRANSFER, TRUE);
curl_setopt($ch, CURLOPT_HEADER, FALSE);
curl_setopt($ch, CURLOPT_POST, TRUE);
curl_setopt($ch, CURLOPT_POSTFIELDS, $fields);
curl_setopt($ch, CURLOPT_SSL_VERIFYPEER, FALSE);
$response = curl_exec($ch);
curl_close($ch);
return $response;
}
$response = sendMessage();
$return["allresponses"] = $response;
$return = json_encode($return);
$data = json_decode($response, true);
print_r($data);
$id = $data['id'];
print_r($id);
print("\n\nJSON received:\n");
print($return);
print("\n");
}//if Node is new
}//func hook signal
我们该怎么做才能为每个缺失值生成一个值?
答案 0 :(得分:2)
您可以创建具有正常值的系列。您应该提取您正在处理的列中的 Nan 值的索引。
df:你的数据框
col:包含 Nan 值的 col
final_lines["list-price"] = final_lines.apply(lambda x: x["list-price"]*-1 if x["ordtype"] != "I" else x["list-price"])
答案 1 :(得分:0)
您可以创建一系列长度与数据帧相同的随机变量,然后应用fillna:
df.fillna(pd.Series([np.random.normal() for x in range(len(df))]))
如果连续缺少一个值,fillna会忽略它。